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, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things 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 MS. 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 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.

在大数据第四次工业革命时代，人工智能（AI）广泛应用于复合材料的各个领域，包括设计分析、材料存储、制造、无损检测、结构健康监测（SHM）及其剩余使用寿命、材料状态 (MS) 和损坏模式的预测。虽然这些 AI 模型被迅速开发并集成到工业物联网中，以跟踪复合材料从出生到死亡的健康状况，但在没有其先前 MS 确定性的情况下，这些集成对于预后仍然不确定。本文是对过去几十年在 SHM 和聚合物基复合材料的预后健康管理领域开发的 AI 模型的全面回顾。它进一步分析了这些发展与这些方法的不确定性本质之间的真正差距。最后，讨论了从出生到死亡的实时预测的管道、混合方法、数据驱动和基于物理的系统的不确定性量化，以及这种复杂的先进复合材料的可靠性标准。本文将重点作为研究人员在用于诊断、预后和控制的复合材料中实施人工智能的基本指南。