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dc.contributor.authorOgorodnyk, Olga
dc.contributor.authorLarsen, Mats
dc.contributor.authorLyngstad, Ole Vidar
dc.contributor.authorMartinsen, Kristian
dc.date.accessioned2020-11-06T13:32:43Z
dc.date.available2020-11-06T13:32:43Z
dc.date.created2020-11-05T10:15:53Z
dc.date.issued2020
dc.identifier.issn2376-5992
dc.identifier.urihttps://hdl.handle.net/11250/2686779
dc.description.abstractInjection molding is a complicated process, and the final part quality depends on many machine and process parameters settings. To increase controllability of the injection molding process, acquisition of the process data is necessary. This paper describes the architecture and development of a prototype of an open application programming interface (API) for injection molding machines (IMMs), which has the potential to be used with different IMMs to log and set the necessary process parameter values. At the moment, the API includes an implementation of EMI data exchange protocol and can be used with ENGEL IMMs with CC300 and RC300 controllers. Data collection of up to 97 machine and process parameters (the number might vary depending on the type of machine at hand), obtained from sensors installed in the machine by the manufacturer is allowed. The API also includes a module for the acquisition of data from additional 3d party sensors. Industrial Raspberry Pi (RevPi) was used to perform analog-to-digital signal conversion and make sensors data accessible via the API prototype. The logging of parameters from the machine and from sensors is synchronized and the sampling frequency can be adjusted if necessary. The system can provide soft real-time communication.en_US
dc.language.isoengen_US
dc.publisherPeerJen_US
dc.relation.urihttps://peerj.com/articles/cs-302/
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleTowards a general application programming interface (API) for injection molding machinesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.journalPeerJ Computer Scienceen_US
dc.identifier.doi10.7717/peerj-cs.302
dc.identifier.cristin1845142
dc.relation.projectNorges forskningsråd: 237900en_US
dc.relation.projectNorges forskningsråd: 256819en_US
dc.description.localcodeDOI 10.7717/peerj-cs.302 Copyright 2020 Ogorodnyk et al. Distributed under Creative Commons CC-BY 4.0en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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