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Access status: Open Access ,
Artificial neural networks as a tool for supporting a moulding sand control system based on the dependency between selected moulding sand properties
(AGH University Press, 2023) Mrzygłód, Barbara; Jakubski, Jarosław; Opaliński, Andrzej; Regulski, Krzysztof
The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters.
Access status: Open Access ,
Comparing deterministic and statistical approaches for predicting »short can« defects in aluminium beverage can production
(Wydawnictwa AGH, 2023) Baran, Wojciech; Regulski, Krzysztof; Kąc, Sławomir; Milenin, Andriy
In the production of beverage cans, »short can« defects in the form of material discontinuities can occur during the deep drawing of cylindrical thin-walled aluminium products. These defects have a significant impact on production efficiency and scrap generation, and their occurrence is influenced by material and process properties. To determine the main influence of material on defect occurrence, two approaches were used: deterministic analysis of mechanical properties and microstructure, as well as statistical processing of production data using decision tree models. The latter approach was found to be more efficient, and a numerical tool was developed based on this approach to predict and reduce defect occurrence in the production process.
Access status: Open Access ,
Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms
(Wydawnictwa AGH, 2022) Olejarczyk-Wożeńska, Izabela; Opaliński, Andrzej; Mrzygłód, Barbara; Regulski, Krzysztof; Kurowski, Wojciech
The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization - Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.

Regulski, Krzysztof

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