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.

翻译：尽管人工智能尤其是深度学习在智能制造的多个方面取得了显著提升，但由于泛化能力不足、高质量训练数据集构建困难以及深度学习方法性能不理想，其广泛应用仍面临挑战。大规模基础模型（LSFMs）的出现引发了人工智能领域的浪潮，推动深度学习模型从单任务、单模态、有限数据模式向涵盖多样化任务、多模态及大规模数据集预训练的范式转变。尽管LSFMs已在多个领域展现出强大的泛化能力、自动生成高质量训练数据集的能力以及卓越性能，但其在智能制造中的应用仍处于起步阶段。目前尚缺乏针对该主题的系统性概述，尤其是关于LSFMs能解决深度学习的哪些挑战，以及如何系统性地应对这些挑战。为填补这一空白，本文系统阐述了LSFMs的现状及其在智能制造背景下的优势，并全面对比了当前深度学习模型在各类智能制造应用中所面临的挑战。我们还概述了利用LSFMs应对这些挑战的路线图。最后，通过展示LSFMs在真实智能制造场景中的应用案例，说明其如何助力工业界提升效率。