Learning Defects From Aircraft NDT Data

Non-destructive evaluation of aircraft production is optimized and digitalized with Industry 4.0. The aircraft structures produced using fiber metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated.

German Aerospace Center (DLR), Augsburg, Germany

Ultrasonic Testing (UT) is a typical Non-destructive testing (NDT) method for examining the structural components for aircraft production. Manufacturing aircraft made of fiber metal laminates (FML) includes cascaded steps such as placement of aluminum, glass prepreg, adhesive, doublers, stringers, vacuum bagging and curing in an autoclave. Quality control (QC) is performed first at the layup of the component (without stringers) after curing and the quality assessment is visually evaluated. The manually performed examination of anomalies is very time-consuming. In addition, conducted NDT inspection using a manual UT phased array for Glass Reinforced (GLARER) FML of A380, it lacked the high capacity of data and additionally an evaluation software.

So, non-destructive evaluation (NDE) 4.0 helps streamline processes, increase quality and lower costs in aircraft production with an automated quality assurance (QA). Traditionally, the quality control of FML is performed by an experienced examiner after the final production of an aircraft structure. But, with the implementation of machine learning (ML) techniques, defects can be identified instantaneously to help the examiner. So, the primary motivation was to develop an automated QA in aircraft production by implementing a machine learning algorithm. The quality analysis process in the proposed method consists of pre analyzing the sensor data acquisition to classify the features according to the defects and good qualities. The proposed approach reduces the examiner's workload, expensive repairs and manufacturing waste.