Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.


Download BibTeX


Comparison of Neural Networks Aiding Material Compatibility Assessment


[ 1 ] Instytut Technologii Materiałów, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • compatibility
  • neural networks
  • classification models
  • expert system
  • materials selection

EN 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.

Date of online publication


Pages (from - to)

14 - 24





Innovations in Mechatronics Engineering

Presented on

International Conference Innovation in Engineering, ICIE 2021, 28-30.06.2021, Guimarães, Portugal

Ministry points / chapter


This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.