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（AI）模型在工程和制造业等领域的应用日益普及，确保这些模型能够为其预测结果提供透明的推理过程变得至关重要。本文提出AI-Reasoner系统，该系统从图像中提取缺陷的形态学特征（DefChars），并利用决策树对DefChars值进行推理。随后，AI-Reasoner通过导出可视化图表和文本解释，为基于掩码的缺陷检测与分类模型的输出结果提供深入分析。此外，该系统还提供有效的数据预处理优化策略及模型性能增强方案。基于包含366张缺陷图像的测试集，我们验证了AI-Reasoner在解释IE Mask R-CNN模型输出方面的有效性。实验结果表明，该系统能成功阐释IE Mask R-CNN模型的预测结果。总体而言，所提出的AI-Reasoner为需要缺陷分析的工业应用场景中AI模型性能提升提供了可行解决方案。