Jaśkowiec Krzysztof, Wilk-Kołodziejczyk Dorota, Bartłomiej Śnieżyński, Reczek Witor, Bitka Adam, Małysza Marcin, Doroszewski Maciej, Pirowski Zenon, Boroń Łukasz
Center of Casting Technology, Łukasiewicz Research Network-Krakow Institute of Technology Contribution, Zakopiańska 73, 30-418 Krakow, Poland.
Faculty of Metals Engineering and Industrial Computer Science and Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland.
Materials (Basel). 2022 Apr 14;15(8):2884. doi: 10.3390/ma15082884.
The aim of the work is to investigate the effectiveness of selected classification algorithms and their extensions in assessing microstructure of castings. Experiments were carried out in which the prepared algorithms and machine learning methods were tested in various conditions and configurations, as well as for various input data, which are photos of castings (photos of the microstructure) or information about the material (e.g., type, composition). As shown by the literature review, there are few scientific papers on this subject (i.e., in the use of machine learning to assess the quality of the microstructure and the obtained strength properties of cast iron). The effectiveness of machine learning algorithms in assessing the quality of castings will be tested using the most universal methods. Results obtained by classic machine learning methods and by neural networks will be compared with each other, taking into account aspects such as interpretability of results, ease of model implementation, algorithm simplicity, and learning time.
这项工作的目的是研究所选分类算法及其扩展在评估铸件微观结构方面的有效性。进行了实验，在各种条件和配置下，以及针对各种输入数据（即铸件照片（微观结构照片）或材料信息（例如类型、成分））对准备好的算法和机器学习方法进行了测试。文献综述表明，关于这个主题（即使用机器学习评估铸铁微观结构质量和所得强度性能）的科学论文很少。将使用最通用的方法测试机器学习算法在评估铸件质量方面的有效性。将相互比较经典机器学习方法和神经网络获得的结果，同时考虑结果的可解释性、模型实现的难易程度、算法的简单性和学习时间等方面。
