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Defining multivariate raw material specifications in industry 4.0

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  • NSTL
  • Elsevier
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.

Industry 40Design spaceModel inversionPartial least squaresPrediction uncertaintyRaw material multivariate specificationsIndustry 40Design spaceModel inversionPartial least squaresPrediction uncertaintyRaw material multivariate specificationsDESIGN SPACE DETERMINATIONPRODUCT DESIGNMODEL INVERSIONREGRESSIONOPTIMIZATIONMANUFACTUREUNCERTAINTYQUALITYREGIONSCHARTS

Borras-Ferris, Joan、Palaci-Lopez, Daniel、Duchesne, Carl、Ferrer, Alberto

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Univ Politecn Valencia

Laval Univ

2022

10.1016/j.chemolab.2022.104563

Chemometrics and Intelligent Laboratory Systems

Chemometrics and Intelligent Laboratory Systems

EISCI
ISSN：0169-7439
年,卷(期)：2022.225
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