End-of-life oriented product design (design for recycling, disassembly, remanufacturing) is considered an emerging area in modern approach to product lifecycle. Numerous tools aiding the design process have been developed, but many of them work as independent computer applications. The presented solution is strictly integrated with typical design environment: CAD 3D and PLM systems. This paper presents the application of agent technology operating in the PLM environment to support the design process. The architecture of the proposed solution is shown. A method of product assessment, based of three indicators, is described. The example analysis of real household appliance is presented

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.