Intelligent recognition of composite material damage based on deep learning and infrared testing.

Composite materials are commonly used in aircraft, and the integrity of these materials affects both flight and safety performance. Damage detection technology involving infrared nondestructive testing has played an important role in damage detection in aircraft composite materials. Traditional manual detection methods are inefficient, and the use of intelligent detection methods can effectively improve detection efficiency.

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning.

With the large-scale application of composite materials in military aircraft, various composite material detection technologies with infrared nondestructive and ultrasonic nondestructive testing as the core have played an important role in detecting composite material component damage in military aircraft. At present, the damage of composite materials is mostly recognized manually, which is time-consuming, laborious, and inefficient. It can effectively improve detection efficiency and accuracy by using intelligent detection methods to detect and recognize damage.

Recognition and detection of aero-engine blade damage based on Improved Cascade Mask R-CNN.

Aero-engine blades are an integral part of the aero-engine, and the integrity of these blades affects the flight performance and safety performance of an aircraft. The traditional manual detection method is time-consuming, labor-intensive, and inefficient. Hence, it is particularly important to use intelligent detection methods to detect and identify damage.