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https://hdl.handle.net/10316/106637
Title: | An integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing | Authors: | Babcinschi, Mihail Freire, Bernardo Ferreira, Lúcia Señaris, Baltasar Vidal, Felix Vaz, Paulo Neto, Pedro |
Keywords: | Interoperability; AutomationML; Additive Manufacturing; Data | Issue Date: | 2020 | Publisher: | Elsevier | Project: | This research was partially supported by European Union's Horizon 2020 under grant agreement No 820776 (project integradde), Portugal 2020 project DM4Manufacturing POCI- 01-0145-FEDER-016418 by UE/FEDER through the program COMPETE 2020, and the Fundac¸ ˜ao para a Ciˆencia e a Tecnologia COBOTIS project (PTDC/EME-EME/32595/2017). This research is also sponsored by FEDER funds through the program COMPETE Programa Operacional Factores de Competitividade, and by national funds through FCT Fundac¸ ˜ao para a Ciˆencia e a Tecnologia under the project UIDB/00285/2020. | Serial title, monograph or event: | Procedia Manufacturing | Volume: | 51 | Abstract: | Increasingly, industry is looking to better integrate their industrial processes and related data. Interoperability is key since the organizations need to share data between them, between departments and the different stages of a given technological process. The problem is that many times there are no standard data formats for data exchange between heterogeneous engineering tools. In this paper we present an integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing (MAM). Data such as the MAM robot targets and process parameters are shared and edited along the different sub-stages of the process, from Computer-Aided Design (CAD), to path planning, to multi-physics simulation, to robot simulation and production. The AutomationML neutral data format allows the implementation of optimization loops connecting different sub-stages, for example the multi-physics simulation and the path planning. A practical use case using the Direct Energy Deposition (DED) process is presented and discussed. Results demonstrated the effectiveness of the proposed AutomationML-based solution. | URI: | https://hdl.handle.net/10316/106637 | ISSN: | 23519789 | DOI: | 10.1016/j.promfg.2020.10.005 | Rights: | openAccess |
Appears in Collections: | I&D CEMMPRE - Artigos em Revistas Internacionais |
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