Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method
- DOI
- 10.1038/s41598-026-52635-z
- Published
- 2026-08-05
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-52635-z,
title = {Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method},
author = {Enrico Bovo and Xi Vincent Wang and Giovanni Lucchetta and Lihui Wang},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-52635-z},
url = {https://doi.org/10.1038/s41598-026-52635-z}
}RIS
TY - JOUR TI - Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method AU - Enrico Bovo AU - Xi Vincent Wang AU - Giovanni Lucchetta AU - Lihui Wang PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-52635-z UR - https://doi.org/10.1038/s41598-026-52635-z ER -
APA
Bovo, E., Wang, X. V., Lucchetta, G., & Wang, L. (2026). Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method. Scientific Reports. https://doi.org/10.1038/s41598-026-52635-z
Source records
- crossref · retrieved 2026-09-25T23:14:51.129Z