TY - JOUR 
A1 - Ruiz Lozano, Leandro
AU - Torres, Manuel
AU - Gómez Vilanova, Alejandro
AU - Díaz Carrillo, Sebastián
AU - González Sesma, José Manuel
AU - Cavas Martínez, Francisco
T1 - Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network
Y1 - 2020
SN - 2076-3417
UR - http://hdl.handle.net/10317/9393
AB - The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on
convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.
KW - Expresión Gráfica en Ingeniería
KW - Advanced manufacturing
KW - Industry 4.0
KW - Poduct development
KW - Product design
KW - Design for X methods
KW - Tolerancing
KW - 3310 Tecnología Industrial
KW - 3313.15 Diseño de Maquinas
LA - eng
PB - MDPI
ER -