%0 Journal Article %1 RamisFerrerAhmadEtAl16at %A Ramis Ferrer, Borja %A Ahmad, Bilal %A Vera, Daniel %A Lobov, Andrei %A Harrison, Robert %A Lastra, José Luis Martínez %D 2016 %J at -- Automatisierungstechnik %K 01624 paper embedded ai factory engineering product processing data information knowledge management zzz.i40 %N 3 %P 231--243 %R 10.1515/auto-2015-0073 %T Product, Process and Resource Model Coupling for Knowledge-driven Assembly Automation %V 64 %X Accommodating frequent product changes in a short period of time is a challenging task due to limitations of the contemporary engineering approach to design, build and reconfigure automation systems. In particular, the growing quantity and diversity of manufacturing information, and the increasing need to exchange and reuse this information in an efficient way has become a bottleneck. To improve the engineering process, digital manufacturing and Product, Process and Resource (PPR) modelling are considered very promising to compress development time and engineering cost by enabling efficient design and reconfiguration of manufacturing resources. However, due to ineffective coupling of PPR data, design and reconfiguration of assembly systems are still challenging tasks due to the dependency on the knowledge and experience of engineers. This paper presents an approach for data models integration that can be employed for coupling the PPR domain models for matching the requirements of products for assembly automation. The approach presented in this paper can be used effectively to link data models from various engineering domains and engineering tools. For proof of concept, an example implementation of the approach for modelling and integration of PPR for a Festo test rig is presented as a case study.

@article{RamisFerrerAhmadEtAl16at, abstract = {Accommodating frequent product changes in a short period of time is a challenging task due to limitations of the contemporary engineering approach to design, build and reconfigure automation systems. In particular, the growing quantity and diversity of manufacturing information, and the increasing need to exchange and reuse this information in an efficient way has become a bottleneck. To improve the engineering process, digital manufacturing and Product, Process and Resource (PPR) modelling are considered very promising to compress development time and engineering cost by enabling efficient design and reconfiguration of manufacturing resources. However, due to ineffective coupling of PPR data, design and reconfiguration of assembly systems are still challenging tasks due to the dependency on the knowledge and experience of engineers. This paper presents an approach for data models integration that can be employed for coupling the PPR domain models for matching the requirements of products for assembly automation. The approach presented in this paper can be used effectively to link data models from various engineering domains and engineering tools. For proof of concept, an example implementation of the approach for modelling and integration of PPR for a Festo test rig is presented as a case study.}, added-at = {2016-12-25T19:57:18.000+0100}, author = {Ramis Ferrer, Borja and Ahmad, Bilal and Vera, Daniel and Lobov, Andrei and Harrison, Robert and Lastra, Jos\'{e} Luis Martínez}, biburl = {https://www.bibsonomy.org/bibtex/26a4527143e3c8ba1856a5fa21569d9c1/flint63}, doi = {10.1515/auto-2015-0073}, file = {de Gruyter:2016/RamisFerrerAhmadEtAl16at.pdf:PDF}, groups = {public}, interhash = {7d728bd56e65d71ccef027b3fdf669e6}, intrahash = {6a4527143e3c8ba1856a5fa21569d9c1}, issn = {0178-2312}, journal = {at -- Automatisierungstechnik}, keywords = {01624 paper embedded ai factory engineering product processing data information knowledge management zzz.i40}, number = 3, pages = {231--243}, timestamp = {2017-07-13T17:47:24.000+0200}, title = {Product, Process and Resource Model Coupling for Knowledge-driven Assembly Automation}, username = {flint63}, volume = 64, year = 2016 }