2026-05-31T22:10:38Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/116092026-05-14T06:40:53Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Maestro Prieto, José Alberto
author
Romero, Pablo E.
author
Ramírez Sanz, José Miguel
author
Bustillo Iglesias, Andrés
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.
spa
Atribución 4.0 Internacional
Single point incremental forming
SPIF
Surface roughness
Semi-supervised learning
Multiple data sources
Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming
info:eu-repo/semantics/article
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URL
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Maestro-jim_2025.pdf