A Chatbot Assistant for Reducing Risk in Machinery Design

DC ElementWertSprache
dc.contributor.authorIyenghar, Padma
dc.contributor.authorKieviet, Michael
dc.contributor.authorPulvermueller, Elke
dc.contributor.authorWuebbelmann, Juergen
dc.contributor.editorDorksen, H.
dc.contributor.editorScanzio, S.
dc.contributor.editorJasperneite, J.
dc.contributor.editorWisniewski, L.
dc.contributor.editorMan, K.F.
dc.contributor.editorSauter, T.
dc.contributor.editorSeno, L.
dc.contributor.editorTrsek, H.
dc.contributor.editorVyatkin, V.
dc.date.accessioned2024-01-04T10:29:00Z-
dc.date.available2024-01-04T10:29:00Z-
dc.date.issued2023
dc.identifier.isbn9781665493130
dc.identifier.issn1935-4576
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72949-
dc.descriptionCited by: 0; Conference name: 21st IEEE International Conference on Industrial Informatics, INDIN 2023; Conference date: 17 July 2023 through 20 July 2023; Conference code: 192026
dc.description.abstractIn this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development. © 2023 IEEE.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE International Conference on Industrial Informatics (INDIN)
dc.subjectArtificial intelligence
dc.subjectArtificial Intelligence (AI)
dc.subjectchatbot
dc.subjectChatbots
dc.subjectFunctional Safety
dc.subjecthazard identification
dc.subjectHazards
dc.subjectMachine design
dc.subjectMachinery design
dc.subjectmachinery functional safety
dc.subjectModel configuration
dc.subjectmodel configurations
dc.subjectNeural network model
dc.subjectNeural network models
dc.subjectRisk assessment
dc.subjectrisk reduction
dc.subjectRisks reduction
dc.subjectSafety awareness
dc.titleA Chatbot Assistant for Reducing Risk in Machinery Design
dc.typeconference paper
dc.identifier.doi10.1109/INDIN51400.2023.10218134
dc.identifier.scopus2-s2.0-85171134385
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171134385&doi=10.1109%2fINDIN51400.2023.10218134&partnerID=40&md5=b422f1421b300174dff341a56e189d4d
dc.description.volume2023-July
dcterms.isPartOf.abbreviationIEEE Int. Conf. Ind. Informatics (INDIN)
local.import.remainsaffiliations : Innotec GmbH, Erlenweg 12, Melle, 49324, Germany; Osnabrueck University of Applied Sciences, Faculty of Engineering and Computer Science, Germany; University of Osnabrueck, Software Engineering Research Group, Germany
local.import.remainscorrespondence_address : P. Iyenghar; Innotec GmbH, Melle, Erlenweg 12, 49324, Germany; email: piyengha@uos.de
local.import.remainspublication_stage : Final
crisitem.author.deptInstitut für Informatik-
crisitem.author.deptidinstitute12-
crisitem.author.parentorgFB 06 - Mathematik/Informatik/Physik-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidPuEl525-
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