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.

翻译：随着人工智能模型在工程和制造等行业的应用日益普及，这些模型需为其预测提供透明化推理依据。本文提出AI-Reasoner框架，通过提取图像中缺陷的形态学特征（DefChars），并利用决策树基于DefChar值进行推理。该框架可生成可视化图表及文本解释，为基于掩膜的缺陷检测与分类模型输出提供可解释性分析，同时提出有效的数据预处理优化策略以提升模型整体性能。采用包含366张缺陷图像的测试集，通过解释IE Mask R-CNN模型输出验证了所提方案的有效性。结果表明，AI-Reasoner能有效解释模型的预测结果。总体而言，该框架为解决工业缺陷分析场景中AI模型性能优化问题提供了可行方案。