Design of experiments and machine learning with application to industrial experiments
Roberto Fontana, Alberto Molena, Luca Pegoraro, Luigi Salmaso
Abstract
Abstract In the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE). The paper aims firstly to review the most suitable designs and ML models to use jointly in an Active Learning (AL) approach; it then reviews ALPERC, a novel AL approach, and proves the validity of this method through a case study on amorphous metallic alloys, where this algorithm is used in combination with a Random Forest model.
Topics & Concepts
Random forestComputer scienceContext (archaeology)Machine learningProduct (mathematics)Artificial intelligenceDesign of experimentsIndustrial engineeringEngineeringMathematicsStatisticsPaleontologyBiologyGeometryIntegrated Circuits and Semiconductor Failure AnalysisProbabilistic and Robust Engineering DesignAdvanced machining processes and optimization