2026-05-31T21:56:08Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/116092026-05-14T06:40:53Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Maestro Prieto, José Alberto 908 600 0000-0002-0018-0094 Romero, Pablo E. 46d23d66-7bae-47be-97b9-d57a0f72328c Ramírez Sanz, José Miguel 750 600 0000-0003-0189-8046 Bustillo Iglesias, Andrés 81 600 0000-0003-2855-7532 2026-05-12T09:06:44Z 2026-05-12T09:06:44Z 2025-10 0956-5515 https://hdl.handle.net/10259/11609 10.1007/s10845-025-02704-3 1572-8145 A lack of experimental data can be especially critical in new manufacturing processes. Although experimental datasets for industrial processes are reported in various research works, their lack of homogeneity complicates any fitting with conventional numerical models. Artificial Intelligence (AI) models can be an optimal alternative to extract useful information from those unconnected datasets, while generating models that can help explain the hidden patterns within datasets and interpret the predictions of the model for final users. Moreover, an AI algorithm that could be trained with limited labeled datasets would be in high demand, as it could effectively lower implementation costs. Semi-Supervised Learning (SSL) techniques might therefore be a promising solution to respond to industrial demand for the analysis of manufacturing processes. In this research, the use of SSL techniques is proposed in a case study of surface quality prediction in single point incremental forming, a promising new manufacturing technique. Datasets were extracted from the existing bibliography to generate a 234-instance dataset with 4 different industrial specifications of roughness. The best results were obtained using a semi-supervised Co-Training algorithm. Semi-supervised methods systematically improved the results obtained with the reference supervised methods, although statistical significance has not been mainly achieved due to the limited dataset size. The results obtained with the unbalanced dataset were very promising for its industrial implementation with an extended training dataset optimized for the range of process conditions of each end-user. Open access funding provided by FEDER European Funds and the Junta de Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027. This work was supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE), the Spanish Ministry of Science and Innovation under projects TED2021-129648B-I00, TED2021-129485B-C43 and PID2020-119894GBI00/AEI/10.13039/501100011033, co-financed through European Union FEDER funds and the “NextGenerationEU/PRTR” Recovery, Transformation and Resilience Plan. Open access funding provided by FEDER European Funds and the Junta De Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027 application/pdf spa Springer Journal of Intelligent Manufacturing. 2025 https://doi.org/10.1007/s10845-025-02704-3 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Single point incremental forming SPIF Surface roughness Semi-supervised learning Multiple data sources Procesos de fabricación Manufacturing processes Inteligencia artificial Artificial intelligence Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Journal of Intelligent Manufacturing https://ror.org/051jb1k20 Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE) hybrid-oa 1 2300,08 EUR 36568439 Springer Nature 2025-07-29 2025-11-14 2025-01-01 2027-12-31 10.1007/s10845-025-02704-3 THUMBNAIL Maestro-jim_2025.pdf.jpg Maestro-jim_2025.pdf.jpg IM Thumbnail image/jpeg 4636 https://riubu.ubu.es/bitstream/10259/11609/4/Maestro-jim_2025.pdf.jpg 7047a43f5d50f7491dee562d1021f2b6 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/11609/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/11609/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Maestro-jim_2025.pdf Maestro-jim_2025.pdf application/pdf 3440692 https://riubu.ubu.es/bitstream/10259/11609/1/Maestro-jim_2025.pdf 06c0e267a9393b8508073b583c12f0f8 MD5 1 10259/11609 oai:riubu.ubu.es:10259/11609 2026-05-14 08:40:53.281 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIA0KQVVUT1JJWkEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSBkaWZ1bmRpciwgZGUgbWFuZXJhIGdyYXR1aXRhLCBlbCBjb250ZW5pZG8gZGUgbG9zIGFyY2hpdm9zIGRpZ2l0YWxlcyBxdWUgY29ycmVzcG9uZGVuIGFsIGRvY3VtZW50byBkZXNjcml0byBhbnRlcmlvcm1lbnRlLCBjb24gY2Fyw6FjdGVyIG5vIGV4Y2x1c2l2byB5IGRlIG1hbmVyYSBww7pibGljYSBlbiBhY2Nlc28gYWJpZXJ0byBhIHRyYXbDqXMgZGUgSW50ZXJuZXQsIHBhcmEgbG8gcXVlIGxhIEJpYmxpb3RlY2EgcHJvY2VkZXLDoSBhIGFyY2hpdmFybG9zIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwuIEFzaW1pc21vIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgZGUgQnVyZ29zIGEgcmVhbGl6YXIgbGFzIHRyYW5zZm9ybWFjaW9uZXMgbmVjZXNhcmlhcyBkZSBmb3JtYXRvLCBubyBkZSBjb250ZW5pZG8sIHBhcmEgZ2FyYW50aXphciBsYSBwcmVzZXJ2YWNpw7NuIHkgZWwgYWNjZXNvIGVuIGVsIGZ1dHVyby4NCg0KRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLg0KDQpMYSBjZXNpw7NuIGRlIGRlcmVjaG9zIGRlIGVzdGEgb2JyYSBzZSBlbmN1ZW50cmEgc3VqZXRhIGEgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3Iu