Szczupak Ewelina, Małysza Marcin, Wilk-Kołodziejczyk Dorota, Jaśkowiec Krzysztof, Bitka Adam, Głowacki Mirosław, Marcjan Łukasz
Faculty of Metals Engineering and Industrial Computer Science, AGH University of Krakow, al. Mickiewicza 30, 30-059 Kraków, Poland.
Łukasiewicz Research Network-Krakow Institute of Technology, Zakopiańska 73, 30-418 Kraków, Poland.
Materials (Basel). 2024 Apr 18;17(8):1873. doi: 10.3390/ma17081873.
The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters.
这项工作展示了一种用于3D打印粉末选择的工具。该项目聚焦于三种类型的粉末，如钢基、镍钴基和铝基粉末。研究过程中的一个重要方面是获取机械参数的可能性。在这项工作中，还检验了使用基于人工智能的选定算法（如随机森林、决策树、K近邻、模糊K近邻、梯度提升、XGBoost、AdaBoost）的可能性。在工作期间，进行了测试以检查哪种算法最适合用于正在开发的决策支持系统。使用了交叉验证以及使用不同评估集进行超参数调整。在这两种情况下，最佳模型都是随机森林，其在交叉验证中的F1指标得分是98.66%，在测试集上调整后为99.10%。该模型可被认为是解决此问题最有前景的模型。第一个结果是对模型在新数据上的表现的更准确估计，而第二个模型则说明了优化后可能的改进或对参数可能的过度训练情况。
