Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/873
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dc.contributor.authorChacko, Mathew-
dc.contributor.authorAtul-
dc.date.accessioned2023-06-20T06:26:39Z-
dc.date.available2023-06-20T06:26:39Z-
dc.date.issued2021-04-05-
dc.identifier.urihttps://doi.org/10.17762/turcomat.v12i2.1805-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/873-
dc.description.abstractDigital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysingsensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. Theproposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of thetools we can detect quality issues and develop an automated quality system using machine learning techniques.en_US
dc.language.isoenen_US
dc.publisherScience Research Societyen_US
dc.subjectCyber-Physical Systemsen_US
dc.subjectMachine Learningen_US
dc.subjectInternet ofThings (IoT)en_US
dc.subjectImage Processingen_US
dc.titleCyber-Physical Quality Systems in Manufacturingen_US
dc.typeArticleen_US
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