# Towards Explainable Augmented Intelligence (AI) for Crack Characterization
## 1 Introduction  
The aim of this paper is to address a challenge of developing an explainable AI for semi-automatic crack characterization, with a view to its ultimate deployment in ultrasonic units for NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. Since the most advanced units are phased arrays of ultrasonic transducers all the experimental data used to train and test the AI discussed below have been collected using linear arrays of this nature. Moreover, the experiments have been designed to emulate cracks and inspection surfaces typically encountered in walls of nuclear reactors. It is particularly important to minimize human involvement in interpretation of NDT data in nuclear industry: With the new nuclear build already under way, NDT practitioners anticipate a severe shortage of suitably qualified and experienced personnel. Also there is pressure in industry for both speeding up the inspections and increasing their reliability. Interestingly, even though ultrasonic inspections have been conducted for decades, a study conducted by TWI (The Welding Institute) a few years ago has demonstrated that although their reliability is high it is not as high as many believe or wish it to be [1]. The most surprising outcome of the study was the fact that human inspectors experienced the greatest difficulty when characterizing large planar cracks. A less surprising finding was that the most difficult cracks to identify were those normal to inspection surface--the responses of their tips are known to be weak. A desire to respond to this study has been another rationale for the work reported here. In order to carry out crack characterization NDT inspectors rely mostly on TOFD (Time of Flight Diffraction) configurations, in which the most prominent features are the diffraction spots surrounding crack tips. By contrast the approaches pursued by those who work towards automating crack characterization often rely on specular reflections. There have been attempts to develop general but time-consuming model-based data processing algorithms, see e.g., [2,3] as well as pure signal processing approaches, such as CS (Compressed Sensing) algorithms [4]. The approach meeting a practical need best seems to be TFM (Total Focusing Method) based on FMC (Full Matrix Capture) [5,6]. Briefly, every element of the Full Matrix is an A-scan (a sequence of ultrasonic pulses) received by an array transducer after this or another array transducer fires a single pulse. TFM uses this matrix to create images that lend themselves to a relatively easy interpretation by both human and artificial intelligence. However, TFM images are often contaminated by noise and various strategies have been offered to modify the TFM algorithm to eliminate false indications [7,8] and reduce noise [8, 9, 10], enabling real-time imaging with portable NDT devices [8,11]. Researchers also began to explore application of machine learning to NDT [12, 13, 14, 15]. However, at present, standard machine learning approaches have limited value: Firstly, most researchers have no access to big data such approaches require and even a few laboratory datasets used below have required a considerable effort and expense to collect. Secondly, standard approaches often lead to results that are unexplainable, and a highly regulated branch of industry, such as NDT of nuclear reactors is unlikely to adopt results of this nature. In this paper we present an alternative: a code that combines a signal processing algorithm based on a simple modification of the TFM with the well-known image processing algorithms as well as a decision tree. The latter is an AI module, which mimics thought processes followed by human inspectors in writing standard inspection reports. The code has been designed to deal with the scatter from large planar cracks, whether specular reflections from crack surfaces or echoes from crack tips. We demonstrate the efficacy of the approach using laboratory data.