Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing Article Swipe
Anna-Maria Schmitt
,
Christian Sauer
,
Dennis Höfflin
,
Andreas Schiffler
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23094183
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23094183
Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception–style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end.
Related Topics
Concepts
Quality assurance
Process (computing)
Segmentation
Computer science
Centroid
Artificial intelligence
Task (project management)
Computer vision
Scope (computer science)
Artificial neural network
Layer (electronics)
Engineering drawing
Engineering
Materials science
Operations management
Systems engineering
Composite material
Programming language
Operating system
External quality assessment
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23094183
- https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785
- OA Status
- gold
- Cited By
- 6
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366829017
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366829017Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23094183Digital Object Identifier
- Title
-
Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal PrintingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-22Full publication date if available
- Authors
-
Anna-Maria Schmitt, Christian Sauer, Dennis Höfflin, Andreas SchifflerList of authors in order
- Landing page
-
https://doi.org/10.3390/s23094183Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785Direct OA link when available
- Concepts
-
Quality assurance, Process (computing), Segmentation, Computer science, Centroid, Artificial intelligence, Task (project management), Computer vision, Scope (computer science), Artificial neural network, Layer (electronics), Engineering drawing, Engineering, Materials science, Operations management, Systems engineering, Composite material, Programming language, Operating system, External quality assessmentTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4366829017 |
|---|---|
| doi | https://doi.org/10.3390/s23094183 |
| ids.doi | https://doi.org/10.3390/s23094183 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37177389 |
| ids.openalex | https://openalex.org/W4366829017 |
| fwci | 0.98136719 |
| type | article |
| title | Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing |
| awards[0].id | https://openalex.org/G6146155381 |
| awards[0].funder_id | https://openalex.org/F4320323888 |
| awards[0].display_name | |
| awards[0].funder_award_id | 41-6562b/25/2-VAL-2103-0006 |
| awards[0].funder_display_name | Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie |
| biblio.issue | 9 |
| biblio.volume | 23 |
| biblio.last_page | 4183 |
| biblio.first_page | 4183 |
| topics[0].id | https://openalex.org/T10783 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2203 |
| topics[0].subfield.display_name | Automotive Engineering |
| topics[0].display_name | Additive Manufacturing and 3D Printing Technologies |
| topics[1].id | https://openalex.org/T10705 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9995999932289124 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2210 |
| topics[1].subfield.display_name | Mechanical Engineering |
| topics[1].display_name | Additive Manufacturing Materials and Processes |
| topics[2].id | https://openalex.org/T12111 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9962999820709229 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2209 |
| topics[2].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[2].display_name | Industrial Vision Systems and Defect Detection |
| funders[0].id | https://openalex.org/F4320323888 |
| funders[0].ror | https://ror.org/00rspzk45 |
| funders[0].display_name | Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C106436119 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6720399856567383 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q836575 |
| concepts[0].display_name | Quality assurance |
| concepts[1].id | https://openalex.org/C98045186 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6577045917510986 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[1].display_name | Process (computing) |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6389169096946716 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6099768877029419 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C146599234 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5872199535369873 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q511093 |
| concepts[4].display_name | Centroid |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.537379264831543 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C2780451532 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5099250078201294 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[6].display_name | Task (project management) |
| concepts[7].id | https://openalex.org/C31972630 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4949766993522644 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[7].display_name | Computer vision |
| concepts[8].id | https://openalex.org/C2778012447 |
| concepts[8].level | 2 |
| concepts[8].score | 0.48382630944252014 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1034415 |
| concepts[8].display_name | Scope (computer science) |
| concepts[9].id | https://openalex.org/C50644808 |
| concepts[9].level | 2 |
| concepts[9].score | 0.46609416604042053 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[9].display_name | Artificial neural network |
| concepts[10].id | https://openalex.org/C2779227376 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4628089964389801 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q6505497 |
| concepts[10].display_name | Layer (electronics) |
| concepts[11].id | https://openalex.org/C199639397 |
| concepts[11].level | 1 |
| concepts[11].score | 0.34162113070487976 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1788588 |
| concepts[11].display_name | Engineering drawing |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.2175624668598175 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C192562407 |
| concepts[13].level | 0 |
| concepts[13].score | 0.14044031500816345 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[13].display_name | Materials science |
| concepts[14].id | https://openalex.org/C21547014 |
| concepts[14].level | 1 |
| concepts[14].score | 0.09463173151016235 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1423657 |
| concepts[14].display_name | Operations management |
| concepts[15].id | https://openalex.org/C201995342 |
| concepts[15].level | 1 |
| concepts[15].score | 0.07625940442085266 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[15].display_name | Systems engineering |
| concepts[16].id | https://openalex.org/C159985019 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q181790 |
| concepts[16].display_name | Composite material |
| concepts[17].id | https://openalex.org/C199360897 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[17].display_name | Programming language |
| concepts[18].id | https://openalex.org/C111919701 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[18].display_name | Operating system |
| concepts[19].id | https://openalex.org/C2778618615 |
| concepts[19].level | 2 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q4008393 |
| concepts[19].display_name | External quality assessment |
| keywords[0].id | https://openalex.org/keywords/quality-assurance |
| keywords[0].score | 0.6720399856567383 |
| keywords[0].display_name | Quality assurance |
| keywords[1].id | https://openalex.org/keywords/process |
| keywords[1].score | 0.6577045917510986 |
| keywords[1].display_name | Process (computing) |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.6389169096946716 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6099768877029419 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/centroid |
| keywords[4].score | 0.5872199535369873 |
| keywords[4].display_name | Centroid |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.537379264831543 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/task |
| keywords[6].score | 0.5099250078201294 |
| keywords[6].display_name | Task (project management) |
| keywords[7].id | https://openalex.org/keywords/computer-vision |
| keywords[7].score | 0.4949766993522644 |
| keywords[7].display_name | Computer vision |
| keywords[8].id | https://openalex.org/keywords/scope |
| keywords[8].score | 0.48382630944252014 |
| keywords[8].display_name | Scope (computer science) |
| keywords[9].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[9].score | 0.46609416604042053 |
| keywords[9].display_name | Artificial neural network |
| keywords[10].id | https://openalex.org/keywords/layer |
| keywords[10].score | 0.4628089964389801 |
| keywords[10].display_name | Layer (electronics) |
| keywords[11].id | https://openalex.org/keywords/engineering-drawing |
| keywords[11].score | 0.34162113070487976 |
| keywords[11].display_name | Engineering drawing |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.2175624668598175 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/materials-science |
| keywords[13].score | 0.14044031500816345 |
| keywords[13].display_name | Materials science |
| keywords[14].id | https://openalex.org/keywords/operations-management |
| keywords[14].score | 0.09463173151016235 |
| keywords[14].display_name | Operations management |
| keywords[15].id | https://openalex.org/keywords/systems-engineering |
| keywords[15].score | 0.07625940442085266 |
| keywords[15].display_name | Systems engineering |
| language | en |
| locations[0].id | doi:10.3390/s23094183 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s23094183 |
| locations[1].id | pmid:37177389 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37177389 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10181250 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | cc-by |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10181250/pdf/sensors-23-04183.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors (Basel) |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10181250 |
| locations[3].id | pmh:oai:doaj.org/article:4f58feed746646ee874dc61a8031d5af |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, Vol 23, Iss 9, p 4183 (2023) |
| locations[3].landing_page_url | https://doaj.org/article/4f58feed746646ee874dc61a8031d5af |
| locations[4].id | pmh:oai:mdpi.com:/1424-8220/23/9/4183/ |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400947 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | True |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | MDPI (MDPI AG) |
| locations[4].source.host_organization | https://openalex.org/I4210097602 |
| locations[4].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[4].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors; Volume 23; Issue 9; Pages: 4183 |
| locations[4].landing_page_url | https://dx.doi.org/10.3390/s23094183 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5023046207 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2256-0741 |
| authorships[0].author.display_name | Anna-Maria Schmitt |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I3130074995 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I3130074995 |
| authorships[0].institutions[0].ror | https://ror.org/01k5h5v15 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I3130074995 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | Technical University of Applied Sciences Würzburg-Schweinfurt |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Anna-Maria Schmitt |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[1].author.id | https://openalex.org/A5077222854 |
| authorships[1].author.orcid | https://orcid.org/0009-0002-3191-9135 |
| authorships[1].author.display_name | Christian Sauer |
| authorships[1].countries | DE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I3130074995 |
| authorships[1].affiliations[0].raw_affiliation_string | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[1].institutions[0].id | https://openalex.org/I3130074995 |
| authorships[1].institutions[0].ror | https://ror.org/01k5h5v15 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I3130074995 |
| authorships[1].institutions[0].country_code | DE |
| authorships[1].institutions[0].display_name | Technical University of Applied Sciences Würzburg-Schweinfurt |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Christian Sauer |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[2].author.id | https://openalex.org/A5022947871 |
| authorships[2].author.orcid | https://orcid.org/0009-0003-5295-6709 |
| authorships[2].author.display_name | Dennis Höfflin |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3130074995 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I3130074995 |
| authorships[2].institutions[0].ror | https://ror.org/01k5h5v15 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I3130074995 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Technical University of Applied Sciences Würzburg-Schweinfurt |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dennis Höfflin |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[3].author.id | https://openalex.org/A5090309786 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1447-7331 |
| authorships[3].author.display_name | Andreas Schiffler |
| authorships[3].countries | DE |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I3130074995 |
| authorships[3].affiliations[0].raw_affiliation_string | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| authorships[3].institutions[0].id | https://openalex.org/I3130074995 |
| authorships[3].institutions[0].ror | https://ror.org/01k5h5v15 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I3130074995 |
| authorships[3].institutions[0].country_code | DE |
| authorships[3].institutions[0].display_name | Technical University of Applied Sciences Würzburg-Schweinfurt |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Andreas Schiffler |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Institute Digital Engineering (IDEE), Technical University of Applied Sciences, Würzburg-Schweinfurt, Ignaz-Schön-Strasse 11, 97421 Schweinfurt, Germany |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10783 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2203 |
| primary_topic.subfield.display_name | Automotive Engineering |
| primary_topic.display_name | Additive Manufacturing and 3D Printing Technologies |
| related_works | https://openalex.org/W2381926679, https://openalex.org/W4241523039, https://openalex.org/W2007009951, https://openalex.org/W2360028903, https://openalex.org/W4280543773, https://openalex.org/W178231042, https://openalex.org/W2082644203, https://openalex.org/W2366083136, https://openalex.org/W2387622493, https://openalex.org/W2350539780 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s23094183 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s23094183 |
| primary_location.id | doi:10.3390/s23094183 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/23/9/4183/pdf?version=1682217785 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s23094183 |
| publication_date | 2023-04-22 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2911861386, https://openalex.org/W2333451148, https://openalex.org/W2045496055, https://openalex.org/W4292819775, https://openalex.org/W3172166634, https://openalex.org/W6791916807, https://openalex.org/W4306391197, https://openalex.org/W6750388188, https://openalex.org/W3021659040, https://openalex.org/W6779937491, https://openalex.org/W6754839354, https://openalex.org/W2900536542, https://openalex.org/W6746237984, https://openalex.org/W3006730468, https://openalex.org/W6780390883, https://openalex.org/W2966005396, https://openalex.org/W1901129140, https://openalex.org/W2531409750, https://openalex.org/W2560311620, https://openalex.org/W2154741421, https://openalex.org/W4226474247, https://openalex.org/W6734027470, https://openalex.org/W2963351448, https://openalex.org/W3033997328, https://openalex.org/W2590124693, https://openalex.org/W2769988472, https://openalex.org/W2892740310, https://openalex.org/W3043597110, https://openalex.org/W2796229351, https://openalex.org/W3041948654 |
| referenced_works_count | 30 |
| abstract_inverted_index.a | 20, 83, 111 |
| abstract_inverted_index.AM | 133 |
| abstract_inverted_index.To | 79 |
| abstract_inverted_index.an | 8, 45 |
| abstract_inverted_index.at | 156 |
| abstract_inverted_index.be | 127 |
| abstract_inverted_index.by | 28, 123 |
| abstract_inverted_index.if | 110 |
| abstract_inverted_index.is | 7, 62, 113 |
| abstract_inverted_index.of | 14, 36, 59, 76, 93 |
| abstract_inverted_index.or | 115 |
| abstract_inverted_index.to | 22, 49, 64, 129, 139 |
| abstract_inverted_index.Due | 138 |
| abstract_inverted_index.The | 56, 103, 120 |
| abstract_inverted_index.and | 32, 42, 53, 72, 97, 144 |
| abstract_inverted_index.are | 106, 154 |
| abstract_inverted_index.bed | 39 |
| abstract_inverted_index.can | 126 |
| abstract_inverted_index.for | 108, 116, 135 |
| abstract_inverted_index.had | 100 |
| abstract_inverted_index.has | 150 |
| abstract_inverted_index.job | 85 |
| abstract_inverted_index.off | 96 |
| abstract_inverted_index.one | 92, 99 |
| abstract_inverted_index.the | 1, 12, 30, 37, 51, 65, 69, 81, 131, 140, 148, 157 |
| abstract_inverted_index.was | 89 |
| abstract_inverted_index.(AM) | 5 |
| abstract_inverted_index.This | 17 |
| abstract_inverted_index.area | 74 |
| abstract_inverted_index.each | 77 |
| abstract_inverted_index.end. | 158 |
| abstract_inverted_index.gain | 23 |
| abstract_inverted_index.into | 25 |
| abstract_inverted_index.part | 54, 112 |
| abstract_inverted_index.some | 151 |
| abstract_inverted_index.task | 10 |
| abstract_inverted_index.them | 94 |
| abstract_inverted_index.this | 124 |
| abstract_inverted_index.used | 128 |
| abstract_inverted_index.were | 40 |
| abstract_inverted_index.with | 86 |
| abstract_inverted_index.work | 125 |
| abstract_inverted_index.area, | 70 |
| abstract_inverted_index.broke | 95 |
| abstract_inverted_index.every | 60 |
| abstract_inverted_index.layer | 61, 67 |
| abstract_inverted_index.metal | 2, 132 |
| abstract_inverted_index.part. | 78 |
| abstract_inverted_index.parts | 88 |
| abstract_inverted_index.print | 84 |
| abstract_inverted_index.scope | 13 |
| abstract_inverted_index.three | 87 |
| abstract_inverted_index.using | 44 |
| abstract_inverted_index.where | 91 |
| abstract_inverted_index.which | 153 |
| abstract_inverted_index.Images | 35 |
| abstract_inverted_index.actual | 31 |
| abstract_inverted_index.areas. | 55 |
| abstract_inverted_index.camera | 142 |
| abstract_inverted_index.chosen | 90 |
| abstract_inverted_index.method | 21, 121 |
| abstract_inverted_index.neural | 47 |
| abstract_inverted_index.powder | 38, 52 |
| abstract_inverted_index.result | 58 |
| abstract_inverted_index.target | 33 |
| abstract_inverted_index.useful | 107 |
| abstract_inverted_index.within | 11 |
| abstract_inverted_index.another | 98 |
| abstract_inverted_index.article | 18 |
| abstract_inverted_index.damaged | 114 |
| abstract_inverted_index.layers. | 34 |
| abstract_inverted_index.limited | 141 |
| abstract_inverted_index.method, | 82 |
| abstract_inverted_index.metrics | 105 |
| abstract_inverted_index.monitor | 130 |
| abstract_inverted_index.network | 48 |
| abstract_inverted_index.predict | 50 |
| abstract_inverted_index.process | 6, 26, 134 |
| abstract_inverted_index.quality | 15, 27, 136 |
| abstract_inverted_index.thermal | 101, 118 |
| abstract_inverted_index.Additive | 3 |
| abstract_inverted_index.approach | 149 |
| abstract_inverted_index.captured | 41 |
| abstract_inverted_index.compared | 63 |
| abstract_inverted_index.evaluate | 80 |
| abstract_inverted_index.insights | 24 |
| abstract_inverted_index.lighting | 146 |
| abstract_inverted_index.presents | 19 |
| abstract_inverted_index.comparing | 29 |
| abstract_inverted_index.detecting | 109 |
| abstract_inverted_index.discussed | 155 |
| abstract_inverted_index.important | 9 |
| abstract_inverted_index.reference | 66 |
| abstract_inverted_index.regarding | 68 |
| abstract_inverted_index.segmented | 43 |
| abstract_inverted_index.Monitoring | 0 |
| abstract_inverted_index.assurance. | 16, 137 |
| abstract_inverted_index.calculated | 104 |
| abstract_inverted_index.centroids, | 71 |
| abstract_inverted_index.difference | 75 |
| abstract_inverted_index.introduced | 122 |
| abstract_inverted_index.normalized | 73 |
| abstract_inverted_index.conditions, | 147 |
| abstract_inverted_index.identifying | 117 |
| abstract_inverted_index.resolutions | 143 |
| abstract_inverted_index.distortions. | 119 |
| abstract_inverted_index.inconsistent | 145 |
| abstract_inverted_index.limitations, | 152 |
| abstract_inverted_index.segmentation | 57 |
| abstract_inverted_index.Manufacturing | 4 |
| abstract_inverted_index.deformations. | 102 |
| abstract_inverted_index.Xception–style | 46 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5023046207 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I3130074995 |
| citation_normalized_percentile.value | 0.70557224 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |