ATENEA for Aerospace Manufacturing

Overview

ATENEA: systems based in artificial intelligence to support manufacturing engineering Contract Art. 83 between AIRBUS D&S and Universidad de Cádiz (CDTI Interconnecta) PI: David Gómez-Ullate (UCA), 01/04/2019 – 31/10/2019, Sum: 90.000 EUR.

Modern aerospace manufacturing demands extremely high quality standards, especially in composite components where defects can be costly and difficult to detect. Within the context of Industry 4.0, ATENEA was a research and innovation project funded by CDTI and developed in collaboration with Airbus, with the goal of bringing data science and artificial intelligence directly into the production and inspection of fan cowls for the Airbus A320/A330 Neo.

The project focused on transforming large volumes of heterogeneous industrial data—machine logs, sensor measurements, manufacturing records, and inspection images—into actionable insights. By integrating predictive analytics, computer vision, and real-time monitoring, ATENEA aimed to improve quality inspection, anticipate manufacturing deficiencies, and reduce non-conformities before they propagated through the production line.

A key outcome of the project was the development of intelligent tools to support both process monitoring and quality assurance, enabling earlier detection of structural defects, better traceability, and more consistent inspection criteria. ATENEA demonstrates how advanced analytics can be embedded into real production environments to enhance reliability, efficiency, and decision-making in aerospace manufacturing.

From a methodological perspective, the project combined predictive modeling and computer vision with industrial data pipelines. Machine-learning models were developed to estimate the probability of non-conformities related to delamination and porosity using data from composite layup machines, environmental sensors, tooling information, and SAP production records. In parallel, image-processing algorithms based on ultrasound inspection data were designed to automatically detect and segment potentially defective regions, producing an objective quality score for inspection reliability. These results were integrated into a real-time dashboard architecture, allowing continuous monitoring of production variables and inspection performance across the manufacturing process.

This was a challenging Industry 4.0 collaboration between Airbus D&S and UCA Datalab at University of Cádiz. I led a team of 4 data scientists and software developers to complete our work package. We made frequent visits to the Airbus production plant at CBC - Puerto de Santa María to work in situ with Airbus personnel, deliver formation courses, become familiar with the whole Fan Cowl production process, etc. Unfortunately, confidentiality requirements due to the sensitive nature of the data and production process did not allow the publication of the results in scientific journals.