TY  - JOUR
IS  - 1
Y1  - 2023/01//
KW  - manufacturing process optimization; aircraft control optimization; statistical process control; teachable machine; process optimization; real-time defect detection
AV  - none
JF  - Drones
VL  - 7
UR  - http://doi.org/10.3390/drones7010031
ID  - uninimx5397
SN  - 2504-446X
A1  - Shafi, Imran
A1  - Mazhar, Muhammad Fawad
A1  - Fatima, Anum
A1  - Álvarez, Roberto Marcelo
A1  - Miró Vera, Yini Airet
A1  - Martínez Espinosa, Julio César
A1  - Ashraf, Imran
TI  - Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance
N2  - 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.
ER  -