Machine learning methods for optimal compatibility of materials in ecodesign

• Autorzy: Rojek I. , Dostatni E.

Identyfikatory

DOI

10.24425/bpasts.2020.131848

• Języki publikacji: EN

Abstrakty

EN

Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. 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 selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.

Słowa kluczowe

EN

machine learning methods; classification models; ecodesign; materials selection; compatibility

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 2
• Strony: 199--206
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Rojek I.

• Kazimierz Wielki University, Institute of Computer Science, 30 Chodkiewicza St., 85-064 Bydgoszcz, Poland

autor

Dostatni E.

• Poznań University of Technology, Faculty of Mechanical Engineering and Management, 5 Maria Skłodowska-Curie Square, 60-965 Poznań, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).