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Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

Wilk-Kołodziejczyk Dorota, Pirowski Zenon, Bitka Adam, Wróbel Kamil, Śnieżyński Bartłomiej, Doroszewski Maciej, Jaśkowiec Krzysztof, Małysza Marcin

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 2

art. no. e73, 2023

The main purpose of the research, presented in this publication, was to develop methodology for the construction of predictive models which allow the selection of material production parameters for the material-technological conversion process. The development of prototype modules based on information-decision system allows an initial assessment of the level of feasibility of undertaking this type of operation. Algorithms 1, 2, 3 presented in the article were used to complete the missing data. The result of the algorithm enabled the creation of a data table that specifies the operation of the predictive models indicated in chapter 3 of this article. Entire work is presented with regard to the background of the ADI cast iron production process to locate the requirement where to apply the developed methods in the field of predictive algorithms and data completion algorithms. On the basis of developed methods and predictive algorithms, trial castings were operated.

Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion

Wilk-Kołodziejczyk Dorota, Jaśkowiec Krzysztof, Bitka Adam, Pirowski Zenon, Grudzień-Rakoczy Małgorzata, Chrzan Konrad, Małysza Marcin, Doroszewski Maciej

Archives of Metallurgy and Materials

2022

Vol. 67, iss. 3

1085--1091

The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.