Computer Methods in Materials Science
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ISSN 2720-4081
e-ISSN: 2720-3948
Issue Date
2022
Volume
Vol. 22
Number
No 4
Description
Journal Volume
Computer Methods in Materials Science
Vol. 22 (2022)
Projects
Pages
Articles
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.
The acoustic emission (AE) controlling method of the electric sheet blanking process - a comparative study of selected data mining methods
(Wydawnictwa AGH, 2022) Kochański, Andrzej Witold; Czyżewski, Piotr; Moszczyński, Leszek
The article presents an experimental stand to assess the state of punch in the process of sheet blanking. Blanking trials were carried out on an eccentric press. During all the trials, there were recorded signals of acoustic emission (AE) that accompanied the process of blanking. For the recorded AE signals, the methodology of data preparation and analysis was presented. On that basis, the results of the assessment of the state of the punch were presented, and they employed five methods of visualization: Andrews curves, Principal Components Analysis, Linear Discriminant Analysis, a modified method of Stochastic Neighbor Embedding and Sammon Mapping. The aim of the work was to assess the possibility of using visualization methods to predict the condition of the tool on the basis of acoustic emission signals in processes carried out in extremely short times.
Soft modelling of the shaping of metal profiles in rapid tube hydroforming technology
(Wydawnictwa AGH, 2022) Sadłowska, Hanna; Kochański, Andrzej Witold
The paper presents an approach to the impact of process parameters in innovative RTH (Rapid Tube Hydroforming) technology for shaping closed metal profiles in flexible and deformable dies. In order to implement the assumed deformation of the deformed profile, the RTH technology requires the monitoring and control of numerous technological parameters, including geometric, material, and technological variables. The paper proposes a two-stage research procedure considering hard modelling (constitutive) and soft modelling (data-driven). Due to the complexity of the technological process, it was required to develop a numerical finite element method FEM model focused on obtaining the adequate profile deformation measured by the ellipsoidality of the cylindrical profile. Based on the results of the numerical experiments, a preliminary soft mathematical model using ANN was developed. Analysing the soft model results, several statistical hypotheses were made and verified to investigate the significance of selected process parameters. Thanks to this, it was possible to select the most important process parameters, i.e., the properties of moulding sands used for RTH dies: the angle of internal friction and cohesion.
Rule modeling of ADI cast iron structure for contradictory data
(Wydawnictwa AGH, 2022) Soroczyński, Artur; Biernacki, Robert; Kochański, Andrzej Witold
Ductile iron is a material that is very sensitive to the conditions of crystallization. Due to this fact, the data on the cast iron properties obtained in tests are significantly different and thus sets containing data from samples are contradictory, i.e. they contain inconsistent observations in which, for the same set of input data, the output values are significantly different. The aim of this work is to try to determine the possibility of building rule models in conditions of significant data uncertainty. The paper attempts to determine the impact of the presence of contradictory data in a data set on the results of process modeling with the use of rule-based methods. The study used the well-known dataset (Materials Algorithms Project Data Library, n.d.) pertaining to retained austenite volume fraction in austempered ductile cast iron. Two methods of rulebased modeling were used to model the volume of the retained austenite: the decision trees algorithm (DT) and the rough sets algorithm (RST). The paper demonstrates that the number of inconsistent observations depends on the adopted data discretization criteria. The influence of contradictory data on the generation of rules in both algorithms is considered, and the problems that can be generated by contradictory data used in rule modeling are indicated.
Computational intelligence based design of biomaterials
(Wydawnictwa AGH, 2022) Vinoth, Arulraj; Datta, Shubhabrata
This paper presents an overview of the applications of computational intelligence techniques, viz. artificial neural networks, fuzzy inference systems, and genetic algorithms, for the design of biomaterials with improved performance. These techniques are basically used for developing data-driven models and for optimization. The paper introduces the domain of biomaterials and how they can be designed using computational intelligence techniques. Then a brief description of the tools is made, followed by the applications of the tools in various domains of biomaterials. The applications range in all classes of materials ranging from alloys to composites. There are examples of applications for the surface treatment of biomaterials, materials for drug delivery systems, materials for scaffolds and even in implant design. It is found the tools can be effectively used for designing new and improved biomaterials.