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<dc:title xml:lang="pl"><![CDATA[Modelling information for the burnishing process in a cyber-physical production system]]></dc:title>
<dc:creator><![CDATA[Patalas-Maliszewska, Justyna]]></dc:creator>
<dc:creator><![CDATA[Posdzich, Marco]]></dc:creator>
<dc:creator><![CDATA[Skrzypek, Katarzyna]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[cyber-physical production system]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[production process]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[burnishing process]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[enterprise resource planning system]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Currently, the manufacturing management board applies technologies in line with the concept of Industry 4.0. Cyber-physical production systems (CPSs) mean integrating computational processes with the corresponding physical ones, i.e., allowing work at the operational level and at the strategic level to run side by side. This paper proposes a framework to collect data and information from a production process, namely, the burnishing one, in order to monitor real-time deviations from the correct course of the process and thus reduce the number of defective products within the manufacturing process.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The proposed new solutions consist of (i) the data and information of the production process, acquired from sensors, (ii) a predictive model, based on the Hellwig method for errors in the production process, relying on indications of a machine status, and (iii) an information layer system, integrating the process data acquired in real time with the model for predicting errors within the production process in an enterprise resource planning (ERP) system, that is, the business intelligence module. The possibilities of using the results of research in managerial practice are demonstrated through the application of an actual burnishing process. This new framework can be treated as a solution which will help managers to monitor the production flow and respond, in real time, to interruptions.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Wiśniewski, Remigiusz - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Gomes, Luis - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Wan, Shaohua - ed.]]></dc:contributor>
<dc:date><![CDATA[2022]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86417/AMCS_2022_32_3_1.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101484/edition/86417/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86417]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 32, number 3 (2022)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=125]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101484]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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