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The International Journal of Advanced Manufacturing Technology

26.01.2023 | ORIGINAL ARTICLE

Lie group dee learning technique to identify the precision errors by map geometry functions in smart manufacturing

verfasst von: Renu Kachhoria, Swati Jaiswal, Smita Khairnar, Kanan Rajeswari, Shailaja Pede, Reena Kharat, Shailesh Galande, Chetan Khadse

Erschienen in: The International Journal of Advanced Manufacturing Technology

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Abstract

Numerous technical disciplines examine the information with a non-Euclidean dimension as its fundamental structural basis. This geometrical information is common in several industries, generally huge and complicated (in virtual groups, also on order to billions). Therefore, they are prime candidates for using artificial intelligence methods. We will employ deeper neuro systems since they are effective solutions for various issues in manufacturing industries, including accurate measurements, precision enabling, and predictive maintenance. Furthermore, the datasets having an inherent Euclidean but a rather grid-like pattern, as well as situations at which invariances of such patterns get integrated into systems employed for simulating those, demonstrate the greatest performance for these methods. The application of AI for smart manufacturing is evolving as algorithms are matured. Modern techniques often superficially discover advantageous Lie grouping characteristics before first representing every action series mostly as a high-dimensional path upon the Lie grouping, including an added crucial moment distortion that causes low accuracy. In this article, we acquire highly suitable Lie grouping characteristics that enable 3D motion identification by integrating the Lie grouping topology into a deeper networking design. We create rotational translating levels inside this networking topology that change the undesirable inputs of the manufacturing parameters with Lie grouping characteristics into those that seem closely correlated as in the timing realm. The group structure has rotating dumping levels for such components, upon which the Lie subgroup helps lower the excessive characteristic dimensions. Therefore, to effectively use normal output levels for such eventual categorization of precision errors, we also suggest a log translation level that transfers the generated multi-dimensional information into contour lines.

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Titel: Lie group dee learning technique to identify the precision errors by map geometry functions in smart manufacturing
verfasst von: Renu Kachhoria
Swati Jaiswal
Smita Khairnar
Kanan Rajeswari
Shailaja Pede
Reena Kharat
Shailesh Galande
Chetan Khadse
Publikationsdatum: 26.01.2023
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-023-10834-2

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