Application of Instance-Based Learning for Cast Iron Casting Defects Prediction

• Autorzy: Robert Sika , Damian Szajewski , Jakub Hajkowski , Paweł Popielarski
• Języki publikacji: EN

Abstrakty

EN

The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors' computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set

Słowa kluczowe

EN

Production engineering; Algorithms; Soft modelling; Casting

PL

Inżynieria produkcji; Algorytmy; Modelowanie miękkie; Odlewnictwo

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2019
• Tom: 10
• Numer: nr 4
• Strony: 101--107

Twórcy

autor

Robert Sika

• Poznan University of Technology, Poland

autor

Damian Szajewski

• Poznan University of Technology, Poland

autor

Jakub Hajkowski

• Poznan University of Technology, Poland

autor

Paweł Popielarski

• Poznan University of Technology, Poland

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Identyfikatory

DOI

10.24425/mper.2019.131450