Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/7211
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dc.contributor.authorLad, Bhupesh Kumaren_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-21T10:53:02Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-21T10:53:02Z-
dc.date.issued2017-
dc.identifier.citationJain, A. K., & Lad, B. K. (2017). Dynamic optimization of process quality control and maintenance planning. IEEE Transactions on Reliability, 66(2), 502-517. doi:10.1109/TR.2017.2684709en_US
dc.identifier.issn0018-9529-
dc.identifier.otherEID(2-s2.0-85018945238)-
dc.identifier.urihttps://doi.org/10.1109/TR.2017.2684709-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/7211-
dc.description.abstractIn this paper, we propose a novel methodology for dynamic optimization of process quality control and maintenance planning while considering the real-Time health state of the system. First, by investigating the relationship between product quality and tool degradation, a new tool condition monitoring (TCM) system for instantaneous diagnostic and prognostic is proposed. Subsequently, the existing process quality control policy is enhanced to become dynamic and extended to deal with machine deterioration with time. This is done via the proposed residual-life based evaluation and multistate magnitude of process shift schemes. Furthermore, the maintenance planning model is modified to capture real-Time remaining life information. These models are integrated and built in conjunction with developed TCM system. As a result, the designed dynamic integrated model can evolve itself to re-evaluate the optimal values for the design parameters used in the entire lifecycle of the manufacturing process. Finally, an experimental case study is implemented to demonstrate the practical feasibility of the developedmethodology.An extensive performance investigation revealed substantial economic benefits over conventional independent approach. This is further complimented with systematic sensitivity analysis. Moreover, we attempt to present potential implications and guidelines for various industrial scenarios to expand the model's robustness and relevance in industrial environment. © 2017 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceIEEE Transactions on Reliabilityen_US
dc.subjectCondition monitoringen_US
dc.subjectMaintenanceen_US
dc.subjectPlanningen_US
dc.subjectProcess controlen_US
dc.subjectQuality assuranceen_US
dc.subjectSensitivity analysisen_US
dc.subjectDynamic optimizationen_US
dc.subjectIndustrial environmentsen_US
dc.subjectIndustrial scenariosen_US
dc.subjectIntegrated modelingen_US
dc.subjectMaintenance planningen_US
dc.subjectManufacturing processen_US
dc.subjectProcess quality controlen_US
dc.subjectTool condition monitoringen_US
dc.subjectQuality controlen_US
dc.titleDynamic optimization of process quality control and maintenance planningen_US
dc.typeJournal Articleen_US
Appears in Collections:Department of Mechanical Engineering

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