MODEL-BASED DECISION SUPPORT FOR VALUE AND SUSTAINABILITY ASSESSMENT: APPLYING MACHINE LEARNING IN AEROSPACE PRODUCT DEVELOPMENT

Abstract

This paper presents a prescriptive approach toward the integration of value and sustainability models in an automated decision support environment enabled by machine learning (ML). The approach allows the concurrent multidimensional analysis of design cases complementing mechanical simulation results with value and sustainability assessment. ML allows to deal with both qualitative and quantitative data and to create surrogate models for quicker design space exploration. The approach has been developed and preliminary implemented in collaboration with a major aerospace sub-system manufacturer.