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Defining multivariate raw material specifications in industry 4.0

Joan Borràs‐Ferrís, Daniel Palací‐López, Carl Duchesne, Alberto Ferrer

2022Chemometrics and Intelligent Laboratory Systems12 citationsDOIOpen Access PDF

Abstract

A novel methodology is proposed for defining multivariate raw material specifications providing assurance of quality with a certain confidence level for the critical to quality attributes (CQA) of the manufactured product. The capability of the raw material batches of producing final product with CQAs within specifications is estimated before producing a single unit of the product, and, therefore, can be used as a decision making tool to accept or reject any new supplier raw material batch. The method is based on Partial Least Squares (PLS) model inversion taking into account the prediction uncertainty and can be used with historical/happenstance data, typical in Industry 4.0. The methodology is illustrated using data from three real industrial processes.

Topics & Concepts

Multivariate statisticsRaw materialPartial least squares regressionComputer scienceQuality assuranceRaw dataProduct (mathematics)Quality (philosophy)Process engineeringData miningIndustrial engineeringReliability engineeringMathematicsOperations managementEngineeringMachine learningOrganic chemistryChemistryGeometryExternal quality assessmentProgramming languageEpistemologyPhilosophyFault Detection and Control SystemsSpectroscopy and Chemometric AnalysesAdvanced Statistical Process Monitoring
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