Mutual Active Learning for Engineering Regulated Statistical Digital Twin Models

Digital twin (DT) models are computational models that can effectively represent different assets and processes in the manufacturing environment. Moreover, the DT models can support intelligent automation by integrating with the digital foundation and the data analytics provided by the cyber-physical system (CPS) in an industrial environment. To properly model a physical process, a DT model should be updated online to closely and timely model the underlying process and reduce modeling uncertainty in the CPS. However, most DT models are created offline and implemented online, which cannot be easily updated by using online data from heterogeneous product designs or manufacturing processes. This limitation arises from existing online learning methods, which are typically designed for identical structures, while real manufacturing CPS involves personalized designs and diverse processes. More importantly, there are limited samples for the same product design or manufacturing process due to manufacturing personalization, which slows down the online updating of DT models. In this article, the authors investigated online DT model updating based on data collected from different product designs and/or processes. The authors proposed a mutual active learning framework to identify informative samples from different designs or processes for online DT model updating. Specifically, by properly balancing the gradient-based features of the DT models and the similarity among these heterogeneous designs or processes, the proposed method can effectively query the most informative samples among heterogeneous processes to update the corresponding DT model in a timely manner. The advantages of the proposed method are illustrated by an engineering-driven statistical DT model for an additive manufacturing process (i.e., fused deposition modeling).

I. Introduction

This research focuses on realizing online model updating of digital twin (DT) models by mutual learning from heterogeneous product designs or manufacturing processes in a cyber-physical system (CPS). The CPS opens new doors for the manufacturing industry since it offers the opportunity to achieve the interconnection between the manufacturing processes and data analytics in virtual and physical worlds, respectively [1]. With the digital foundation in the CPS to passively collect data and information from manufacturing materials, processes, and environments, behaviors of complex manufacturing assets and processes can be effectively explored, monitored, analyzed, and optimized by cutting-edge data analysis models [2]. Specifically, DT models have been considered as a paradigm of computational models in the CPS to establish a comprehensive physical and functional description of assets and processes [3]. Nowadays, DT models have been widely used in different engineering application areas, such as engineering-based simulation for product design validation [4], product life-cycle management [5], and predictive maintenance [6].