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Comparison of Neural Networks Aiding Material Compatibility Assessment

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Innovations in Mechatronics Engineering (icieng 2021)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

A new method of selection of materials at the design step is presented in this paper. The method takes into recyclability of materials. The authors compare the effectiveness of neural networks (a multilayer perceptron, radial basis function networks, and self-organizing feature map - SOFM networks) as modelling tools aiding the selection of compatible materials in ecodesign. The best artificial neural networks were used in an expert system. The input data for the selection of materials was start point to initiate the study. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using the selected neural networks. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models aiding the compatible materials selection help boost the recycling properties of designed products. Neural networks are a very good tool to support the selection of materials in the ecodesign. This has been proven in the article.

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Acknowledgments

The work presented in the paper has been co-financed under 0613/SBAD/8727 and grants to maintain research potential Kazimierz Wielki University.

Author information

Authors and Affiliations

  1. Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

    Izabela Rojek & Piotr Kotlarz

  2. Faculty of Mechanical Engineering, Poznan University of Technology, Poznan, Poland

    Ewa Dostatni

Authors
  1. Izabela Rojek
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  2. Ewa Dostatni
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  3. Piotr Kotlarz
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Corresponding author

Correspondence to Izabela Rojek .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Minho, Guimarães, Portugal

    José Machado

  2. Department of Industrial Electronics, University of Minho, Guimarães, Portugal

    Filomena Soares

  3. Poznan University of Technology, Poznan, Poland

    Justyna Trojanowska

  4. Department of Mechatronics Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey

    Sahin Yildirim

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Rojek, I., Dostatni, E., Kotlarz, P. (2022). Comparison of Neural Networks Aiding Material Compatibility Assessment. In: Machado, J., Soares, F., Trojanowska, J., Yildirim, S. (eds) Innovations in Mechatronics Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-79168-1_2

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