Physics-based and data-driven hybrid modeling in manufacturing: a review

Abstract

ABSTRACTManufacturing, an industry set in the physical world, is undergoing its digital transformation, also known as the fourth industrial revolution. Sensors, connectivity, and platforms provide unprecedented access to quantify the quality and diversity of manufacturing data. Progress in data-driven modeling is exponential across all industries. This leads to the question of how physics-based and data-driven modeling can be utilized in a hybrid modeling approach to advance our understanding of processes, materials, and systems in manufacturing. In this review, we focus on discrete manufacturing based on the understanding that hybrid modeling is more mature in process manufacturing. This paper aims to provide an overview of projects where hybrid modeling was used in manufacturing and introduce various ways of composing hybrid models. We provide examples highlighting the implementation of models, structure and expand on metrics to test and validate hybrid models, discuss challenges, and future research directions of hybrid modeling in manufacturing.

Keywords

• AI
• machine learning
• hybrid modeling
• smart manufacturing
• hybrid analytics