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access icon openaccess Base types selection of PSS based on a priori algorithm and knowledge-based ANN

Manufacturers tend to bundle a product with its related services as a product service system (PSS), to create more values for customers and gain competitive advantages for themselves. Configuration design is the key process of PSS development. Configuring a PSS involves selecting and combining appropriate product and service components, to satisfy individual customer requirements. This study studies the mapping relationship between customer requirement attributes and PSS base types in PSS for CNC machine tools, which provides a great reference value for engineers in configuration design. Owing to the high complexity and non-linearity between customer requirement and product, an integrated intelligent learning method based on a priori algorithm and knowledge-based artificial neural network (ANN) is proposed in this study. First, the data of historical configuration instance data sets are processed and then a priori algorithm is used to extract the effective rules as domain knowledge. Domain knowledge is used to build the initial structure of ANN. Moreover, data sets are used to further optimize the network. The knowledge-based ANN is used to realize the mapping between customer requirement attributes and PSS base types. The proposed method is validated in the selection of the PSS base type for CNC machine tools.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cim.2018.0003
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content/journals/10.1049/iet-cim.2018.0003
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