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Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

1
Department of Industrial Engineering, University of Bologna, IT-40136 Bologna, Italy
2
Jožef Stefan Institute, SL-1000 Ljubljana, Slovenia
3
Department of Material Engineering, Federal University of Rio Grande do Sul, Porto Alegre BR-90040-060, Brazil
*
Author to whom correspondence should be addressed.
Metals 2019, 9(5), 557; https://doi.org/10.3390/met9050557
Received: 18 April 2019 / Revised: 4 May 2019 / Accepted: 9 May 2019 / Published: 13 May 2019
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Abstract

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed. View Full-Text
Keywords: material properties prediction; experimental data analysis; ductile/spheroidal cast iron (SGI); compact graphite cast iron (CGI); Machine Learning (RF); pattern recognition; Random Forest (RF); Artificial Neural Network (NN); k-nearest neighbours (kNN) material properties prediction; experimental data analysis; ductile/spheroidal cast iron (SGI); compact graphite cast iron (CGI); Machine Learning (RF); pattern recognition; Random Forest (RF); Artificial Neural Network (NN); k-nearest neighbours (kNN)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fragassa, C.; Babic, M.; Bergmann, C.P.; Minak, G. Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data. Metals 2019, 9, 557.

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