RT info:eu-repo/semantics/article
T1 Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming
A1 Maestro Prieto, José Alberto
A1 Romero, Pablo E.
A1 Ramírez Sanz, José Miguel
A1 Bustillo Iglesias, Andrés
K1 Single point incremental forming
K1 SPIF
K1 Surface roughness
K1 Semi-supervised learning
K1 Multiple data sources
K1 Procesos de fabricación
K1 Manufacturing processes
K1 Inteligencia artificial
K1 Artificial intelligence
AB 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.
PB Springer
SN 0956-5515
YR 2025
FD 2025-10
LK https://hdl.handle.net/10259/11609
UL https://hdl.handle.net/10259/11609
LA spa
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 01-jun-2026