Artículo
Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study
Autor/es | Lorenzo Espejo, Antonio
Escudero Santana, Alejandro Muñoz Díaz, María Luisa Robles-Velasco, Alicia |
Departamento | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II |
Fecha de publicación | 2022-07-01 |
Fecha de depósito | 2022-10-24 |
Publicado en |
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Resumen | This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and ... This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability. |
Agencias financiadoras | FEDER 0754_CIU3A_5_A Agencia para la Innovación y el Desarrollo de Andalucía (IDEA) 802C2000003 Ministerio de Universidades FPU20/05584 |
Identificador del proyecto | 0754_CIU3A_5_A
802C2000003 FPU20/05584 |
Cita | Lorenzo Espejo, A., Escudero Santana, A., Muñoz Díaz, M.L. y Robles Velasco, A. (2022). Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study. Sustainability, 14 (13), 7779. https://doi.org/10.3390/su14137779. |
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