Search results for "Surface-Roughness"

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Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

2018

International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…

0209 industrial biotechnologyDesignComputer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreBayesian inferenceIndustrial and Manufacturing EngineeringArticle[SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineeringanalyticsUncertainty quantificationMonte-Carlouncertaintycomputer.programming_languageParsingBayesian networkInformationSystems_DATABASEMANAGEMENTstandardPython (programming language)XMLComputer Science ApplicationsmanufacturingComputingMethodologies_PATTERNRECOGNITIONBayesian networksControl and Systems EngineeringSurface-RoughnessData analysisPredictive Model Markup Language020201 artificial intelligence & image processingData miningcomputerXML
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A methodology for the semi-automatic generation of analytical models in manufacturing

2018

International audience; Advanced analytics can enable manufacturing engineers to improve product quality and achieve equipment and resource efficiency gains using large amounts of data collected during manufacturing. Manufacturing engineers, however, often lack the expertise to apply advanced analytics, relying instead on frequent consultations with data scientists. Furthermore, collaborations between manufacturing engineers and data scientists have resulted in highly specialized applications that are not relevant to broader use cases. The manufacturing industry can benefit from the techniques applied in these collaborations if they can be generalized for a wide range of manufacturing probl…

Optimization0209 industrial biotechnologySupport Vector MachineGeneral Computer ScienceProcess (engineering)Computer sciencemedia_common.quotation_subjectResource efficiencyComputerApplications_COMPUTERSINOTHERSYSTEMS02 engineering and technology020901 industrial engineering & automationManufacturing0202 electrical engineering electronic engineering information engineeringAdvanced analytics[INFO]Computer Science [cs]Quality (business)Use caseMillingmedia_commonGenetic AlgorithmArtificial Neural-Networkbusiness.industrySystemsGeneral EngineeringModel-basedNeural networkRegressionManufacturing engineeringProduct (business)ManufacturingSurface-RoughnessAnalytics020201 artificial intelligence & image processingDynamic Bayesian NetworksPerformance indicatorFault-DiagnosisPredictionbusinessComputers in Industry
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