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

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 07/08/2018, Accepted 13/02/2019, Revised 01/02/2019, Published 21/02/2019

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.

Inspec keywords: machine tools; customer services; computerised numerical control; product customisation; production engineering computing; neural nets; learning (artificial intelligence)

Other keywords: a priori algorithm; product service system; related services; combining appropriate product; individual customer requirements; configuration design; knowledge-based ANN; PSS base type; PSS development; knowledge-based artificial neural network; historical configuration instance data sets; base types selection; customer requirement attributes; domain knowledge; CNC machine tools; selecting

Subjects: Combinatorial mathematics; Production equipment; Neural computing techniques; Industrial applications of IT; Production engineering computing; Knowledge engineering techniques

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