Browsing by Author "Perzyk, Marcin"

Now showing 1 - 2 of 2

Access status: Open Access ,
Fundamentals of a recommendation system for the aluminum extrusion process based on data-driven modeling
(Wydawnictwa AGH, 2022) Perzyk, Marcin; Kochański, Andrzej Witold; Kozłowski, Jacek
The aluminum profile extrusion process is briefly characterized in the paper, together with the presentation of historical, automatically recorded data. The initial selection of the important, widely understood, process parameters was made using statistical methods such as correlation analysis for continuous and categorical (discrete) variables and »inverse« ANOVA and Kruskal-Wallis methods. These selected process variables were used as inputs for MLP-type neural models with two main product defects as the numerical outputs with values 0 and 1. A multi-variant development program was applied for the neural networks and the best neural models were utilized for finding the characteristic influence of the process parameters on the product quality. The final result of the research is the basis of a recommendation system for the significant process parameters that uses a combination of information from previous cases and neural models.
Access status: Open Access ,
Machine learning methods for diagnosing the causes of die-casting defects
(Wydawnictwa AGH, 2023) Okuniewska, Alicja; Perzyk, Marcin; Kozłowski, Jacek
The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world's leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.