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Cited by 584Impact of AI and Big Data on Business and SocietyDigital Transformation in IndustryArtificial Intelligence Applications

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

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Arinez, Jorge F., Chang, Qing, Gao, Robert X., Xu, Chengying, Zhang, Jianjing·2020·Journal of Manufacturing Science and Engineering
DOI 10.1115/1.4047855
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Abstract

Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

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  • Together, AI, ML, and DL have enabled the development of robots that can perform a wide range of tasks, from simple pick-and-place operations to complex manipulation and navigation in unstructured environments.

    “Together, these technologies have enabled the development of robots that can perform a wide range of tasks, from simple pick-and-place operations to complex manipulation and navigation in unstructured environments [5] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [5]

    Section 3.1 (page unavailable) links advances in robot sensing, perception, control, teaching, and learning to greater flexibility and adaptability. Sections 3.4.1 and 3.4.6 describe AI-assisted planning in uncertain environments and collaborative assembly, but the copy does not establish the claimed range from pick-and-place to complex manipulation and navigation in unstructured environments. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J.
Affiliations
  • General Motors (United States) (US)
  • University of Virginia (US)
  • Case Western Reserve University (US)
  • North Carolina State University (US)
ORCID
Venue
Journal of Manufacturing Science and Engineering
Publisher
ASME International
Published
2020-08-13
Volume
142
Issue
11
ISSN
  • 1087-1357 (ISSN-L)
  • 1528-8935
DOI
10.1115/1.4047855
OpenAlex
W3043941844
Cited by
584
References
6
Citations by year
2026: 88 · 2025: 157 · 2024: 163 · 2023: 91 · 2022: 47 · 2021: 35 · 2020: 3
FWCI
26.55
Type
journal article
Language
en
Open access
Licence
www.asme.org/publications-submissions/publishing-information/legal-policies (vor)
Locations
Journal of Manufacturing Science and Engineering (publishedVersion)
Funders
Directorate for Engineering (1351160, 1830295, 1853454)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence in manufacturing, machine learning, big data analytics, production data, supervisory control, process monitoring, fault diagnosis, prognosis, materials engineering

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