  Although the applications of artificial intelligence especially deep learning
had greatly improved various aspects of intelligent manufacturing, they still
face challenges for wide employment due to the poor generalization ability,
difficulties to establish high-quality training datasets, and unsatisfactory
performance of deep learning methods. The emergence of large scale foundational
models(LSFMs) had triggered a wave in the field of artificial intelligence,
shifting deep learning models from single-task, single-modal, limited data
patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training
on massive datasets. Although LSFMs had demonstrated powerful generalization
capabilities, automatic high-quality training dataset generation and superior
performance across various domains, applications of LSFMs on intelligent
manufacturing were still in their nascent stage. A systematic overview of this
topic was lacking, especially regarding which challenges of deep learning can
be addressed by LSFMs and how these challenges can be systematically tackled.
To fill this gap, this paper systematically expounded current statue of LSFMs
and their advantages in the context of intelligent manufacturing. and compared
comprehensively with the challenges faced by current deep learning models in
various intelligent manufacturing applications. We also outlined the roadmaps
for utilizing LSFMs to address these challenges. Finally, case studies of
applications of LSFMs in real-world intelligent manufacturing scenarios were
presented to illustrate how LSFMs could help industries, improve their
efficiency.
