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广泛，确保这些模型能够为其预测提供透明的推理逻辑变得至关重要。本文提出了一种名为AI-Reasoner的推理系统，该系统从图像中提取缺陷的形态特征（DefChars），并利用决策树基于这些特征值进行推理。随后，AI-Reasoner通过生成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提供可解释性见解。此外，该系统还提供了有效的数据预处理优化策略及模型性能改进方案。我们使用包含366张缺陷图像的测试集，对IE Mask R-CNN模型的输出进行了解释性验证。实验结果表明，AI-Reasoner能够有效解释该模型的预测结果。总体而言，本文提出的AI-Reasoner为工业应用中需要缺陷分析的AI模型提供了一种性能提升解决方案。