Agent-Based Decision-Information System Supporting Effective Resource Management of Companies

Koźlak, Jarosław; Śnieżyński, Bartłomiej; Wilk-Kołodziejczyk, D.; Kluska-Nawarecka, S.; Jaśkowiec, Krzysztof; Żabińska, Małgorzata

doi:10.1007/978-3-319-98443-8_28

Cited by 2 publications

(1 citation statement)

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“…Research is also being conducted to use artificial intelligence methods to optimize parameters and predict the mechanical properties of these 3D printed components [6][7][8][9][10]. Examples of intelligent decision support systems are available in the literature [11][12][13][14][15] for supporting the planning of technological processes of machining and 3D printing and others technology. Artificial intelligence methods (e.g., neural networks) are also used in the context of material selection [16].…”

Section: Introductionmentioning

confidence: 99%

Decision Support Tool in the Selection of Powder for 3D Printing

Szczupak,

Małysza,

Wilk-Kołodziejczyk

et al. 2024

Materials

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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.

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