Artificial Intelligence in Real-Time Diagnostics and Prognostics of Composite Materials and its Uncertainties – a Review

Abstract

**Please note that the full text is embargoed** ABSTRACT: In the era of the 4th industrial revolution of big data, Artificial Intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing (NDT), Structural Health Monitoring (SHM) and Prognostics of its Remaining Useful Life (RUL), Material State (MS) and damage modes. While these AI models are rapidly developed and integrated into the Industrial Internet of Things (IIoT) to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous material state. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and Prognostics Health Management (PHM) of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for Diagnosis, Prognosis, and Control. [This is an original manuscript / preprint of an article published by IOP Publishing Ltd. in Smart Materials and Structures on June 23 2021, available online: https://doi.org/10.1088/1361- 665X/ac099f ] [As the Version of Record of this article is going to be / has been published on a subscription basis, this Accepted Manuscript is available for reuse under a CC BY-NC-ND 3.0 licence after the 12 month embargo period.]