Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Artículos y libros Abierto Inglés Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively. metadata Shafi, Imran; Mazhar, Muhammad Fawad; Fatima, Anum; Álvarez, Roberto Marcelo; Miró Vera, Yini Airet; Martínez Espinosa, Julio César y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, ulio.martinez@unini.edu.mx, SIN ESPECIFICAR (2023) Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance. Drones, 7 (1). p. 31. ISSN 2504-446X