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

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

Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming Maestro Prieto, José Alberto Romero, Pablo E. Ramírez Sanz, José Miguel Bustillo Iglesias, Andrés Single point incremental forming SPIF Surface roughness Semi-supervised learning Multiple data sources 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. 2026-05-12T09:06:44Z 2026-05-12T09:06:44Z 2026-05-12T09:06:44Z 2025-10 info:eu-repo/semantics/article 0956-5515 https://hdl.handle.net/10259/11609 10.1007/s10845-025-02704-3 1572-8145 spa Journal of Intelligent Manufacturing. 2025 https://doi.org/10.1007/s10845-025-02704-3 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Springer