Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

The Application of Deep Learning in Quality Assurance for U.S. Manufacturing

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  • Dr. Ayesha Patel
Dr. Ayesha Patel
Director of AI Research, Google AI, Mountain View, USA

Published 23-09-2024

Keywords

  • Quality Assurance
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Ayesha Patel, “The Application of Deep Learning in Quality Assurance for U.S. Manufacturing”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 158–178, Sep. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/158

Abstract

Quality of products has always been a major concern for companies, and the advent of deep learning is adding more value and efficiency to quality assurance as it does in many other applications. Specifically, in the U.S., manufacturing has always had stringent quality assurance guidelines and has previously been aided by other forms of machine learning. The scope of this essay is to present how deep learning has been applied to complex, large-scale quality assurance systems to better inform U.S. producers' potential for employing these new technologies. In the following section, the necessary background for this new development is laid out: a primer into deep learning and its practical application, as well as an overview of why quality assurance is and will continue to be vital for U.S. manufacturing.

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