金相组织智能识别技术在材料工程教学中的应用与实践——以《材料科学基础》课程为例

Application and Practice of Intelligent Metallographic Structure Recognition Technology in Materials Engineering Teaching—A Case Study of the Course “Fundamentals of Materials Science”

摘要: 金相组织识别是材料工程专业教学的核心内容之一，但其抽象性、复杂性及相似性高等特点，导致传统教学中存在学生接受度低、学习门槛高、实践效果差等问题。为解决这一教学难题，本文将合金金相智能识别技术引入《材料科学基础》课堂教学，通过课堂搭建“钢铁组织金相智能识别模型”、组织学生参与模型训练、开展模型与学生识别效果比拼等教学实践，探索智能化教学模式对提升教学效率与质量的作用。研究结果表明，借助卷积神经网络等AI技术，能够有效降低金相组织识别的学习难度，激发学生学习兴趣，显著提升学生对金相组织知识的理解与应用能力，为材料工程专业课程教改提供了新思路与实践参考。

Abstract: Metallographic structure recognition is a core component of materials engineering education. However, its abstract nature, inherent complexity, and the high similarity among various microstructures often lead to low student acceptance, a steep learning curve, and unsatisfactory practical outcomes in traditional teaching settings. To address these pedagogical challenges, this study introduces an intelligent recognition system for alloy metallographic structures into the classroom teaching of Fundamentals of Materials Science. Through a series of instructional practices—including the in-class construction of an “intelligent recognition model for steel microstructures”, student participation in model training, and comparative competitions between the model and students—this paper explores the effectiveness of intelligent teaching models in enhancing instructional efficiency and quality. The results demonstrate that the adoption of AI techniques, particularly convolutional neural networks, can effectively reduce the difficulty of learning metallographic structure recognition, stimulate student interest, and significantly improve their understanding and application skills. This study offers new insights and practical references for the reform of materials engineering curricula.