Industrial Artificial Intelligence for industry 4.0-based manufacturing systems
By Jay H. Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare. Manufacturing Letters, 2018
681 citations recorded by the source corpus.
Abstract The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), this paper provides an insight into the current state of AI technologies and the eco-system required to harness the power of AI in industrial applications.
Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/0685c8eb9ce24ff32e726b18a0f394dbbcc88d75
Citations in the corpus
- Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “The expectation of AI has also been realigned to the more focused perspective of enabling analytics , whereby AI tools are a complement to the domain knowledge of human experts in the factory rather than their replacement [6 – 13].”
- A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021, neutral): “AI is increasingly incorporated at work to improve task execution and performance (Lee, Davari, Singh, & Pandhare, 2018; Von Krogh, 2018), and it is associated with computer-based systems and applications involving, among other things, machine learning (Chui, Manyika, & Miremadi, 2015), soft…”
- Prescriptive analytics: Literature review and research challenges (2020, neutral): “Further, there is a trend of applying artificial intelligence methods and tools to prescriptive analytics (Lee, Davari, Singh, & Pandhare, 2018).”