%0 Conference Paper %1 noauthororeditor2018cloudbased %A Lindemann, Benjamin %A Karadogan, Celalettin %A Jazdi, Nasser %A Liewald, Mathias %A Weyrich, Michael %B 3rd IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control – CESCIT 2018 %D 2018 %K ias %T Cloud-based Control Approach in Discrete Manufacturing Using a Self-Learning Architecture %U https://www.ias.uni-stuttgart.de/dokumente/publikationen/2018_Cloud-based-Control-Approach-in-Discrete-Manufacturing-Using-a-Self-Learning-Architecture.pdf %X Automated manufacturing machines in the discrete manufacturing domain are frequently facing changes in environmental conditions such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical and software changes. In industrial practice, these adaptation processes are individually performed by experts without methodological support which is time-consuming and highly error-prone. This article proposes a structured approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

@inproceedings{noauthororeditor2018cloudbased, abstract = {Automated manufacturing machines in the discrete manufacturing domain are frequently facing changes in environmental conditions such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical and software changes. In industrial practice, these adaptation processes are individually performed by experts without methodological support which is time-consuming and highly error-prone. This article proposes a structured approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.}, added-at = {2018-08-06T10:34:05.000+0200}, author = {Lindemann, Benjamin and Karadogan, Celalettin and Jazdi, Nasser and Liewald, Mathias and Weyrich, Michael}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2476b2108363993166e4dbc98c7e1bb1b/sekretariatias}, booktitle = {3rd IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control – CESCIT 2018}, interhash = {844f2d794dbea90c740207d7de5febcb}, intrahash = {476b2108363993166e4dbc98c7e1bb1b}, keywords = {ias}, timestamp = {2018-08-06T08:37:24.000+0200}, title = {Cloud-based Control Approach in Discrete Manufacturing Using a Self-Learning Architecture }, url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2018_Cloud-based-Control-Approach-in-Discrete-Manufacturing-Using-a-Self-Learning-Architecture.pdf}, year = 2018 }