Journal of Airline Operations and Aviation Management

Thanks to developments in machine learning (ML), particularly deep learning, which now has the greatest performance among algorithms, narrow artificial intelligence, often known as "weak AI," has progressed during the past few years. further machine learning for deep learning to provide potent model parameters that can forecast the occurrence of certain events in the future, massive volumes of data, also referred to as "big data” and must be rapidly collected. Such a big damage event dataset is not accessible in many other fields, such as visual inspection of aeroplanes, and this makes it difficult to train deep learning algorithms to work effectively. Good at spotting physical damage to aircraft structures. In order to reach this human-level intelligence in aircraft damage inspection, it is possible to include inductive bias into deep learning. This paper provides an illustration of how to incorporate expertise in aircraft engineering into the creation of deep learning algorithms. The effectiveness of our method, which builds a deep convolutional neural network that categorises crack lengths based on break propagation curves acquired from fatigue testing, was shown on aerospace grade aluminium samples.