  As the use of artificial intelligent (AI) models becomes more prevalent in
industries such as engineering and manufacturing, it is essential that these
models provide transparent reasoning behind their predictions. This paper
proposes the AI-Reasoner, which extracts the morphological characteristics of
defects (DefChars) from images and utilises decision trees to reason with the
DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e.
charts) and textual explanations to provide insights into outputs made by
masked-based defect detection and classification models. It also provides
effective mitigation strategies to enhance data pre-processing and overall
model performance. The AI-Reasoner was tested on explaining the outputs of an
IE Mask R-CNN model using a set of 366 images containing defects. The results
demonstrated its effectiveness in explaining the IE Mask R-CNN model's
predictions. Overall, the proposed AI-Reasoner provides a solution for
improving the performance of AI models in industrial applications that require
defect analysis.
