Advanced artificial intelligence system by intuitionistic fuzzy (sic)-subring for automotive robotic manufacturing

In recent years, robotic engineering has been enriched with Artificial Intelligence (AI) technology, preparing the industries to enter the Industry 4.0 era. The powerful neoteric paradigm of AI can serve automotive industries (as one of the largest sectors in the world), to inevitably change their outdated manufacturing strategies. These industrial sectors are increasingly encountering mega data that inevitably carry uncertainty, for which the available methodologies are not capable to deal with that efficiently. To theoretically resolve this gap, a generalized intuitionistic fuzzy set (IFS) theory is proposed here as an efficient, fast, and flexible method. Based on the membership and non-membership degrees, multi-aspect gamma-systems is developed to model the complex real systems. Inspired by multi-attribute gamma-systems and IFS approach, a novel mathematical concept namely intuitionistic fuzzy gamma-subring (IF gamma R) method, is developed to establish an AI platform for robotic automotive manufacturing. Significant characteristics of IF gamma R are developed, including the overlapping of elements with IF gamma R property is IF gamma R, also image and inverse image of elements with IF gamma R property are IF gamma R under gamma-ring homomorphism. Additionally, the connection between upper and lower bound level cuts and image/inverse image property are parametrically discussed. With the effect of surjective homomorphism on upper and lower level cuts, there would be equivalent upper and lower level cuts of image/inverse image in IF gamma R environment. The developed notion of IF gamma I is obtained as the generalization of gamma-ideal under gamma-ring R along with the resultant fundamental properties of IF gamma I, where the overlapping/intersection family of IF gamma Is is proved to be IF gamma I. Also, the upper and lower bound level cuts of elements with IF gamma I property are gamma-ideals. Finally, the proposed IF gamma gamma R method is utilized for automotive AI systems (AAIS) by means of mathematical algebraic notions of gamma-ring, IFS, gamma-ring isomorphism, and upper and lower bound levels. The developed methodology is validated using real dataset of industrial robots in supply chain and then, the elements are characterized in terms of metric overall factory effectiveness. With a systematic pattern of gamma-ring structure, the IF gamma R model is accomplished on elements, and the intercomponent correspondence of AAIS is established with the gamma-ring isomorphism. Based on QC (quality criteria) and non-QC indexes, as the derivation of upper and lower bound level cuts, the analysis of parameters (robots) is simplified for the identification of effective and compatible components in AAIS. The generalized IFS-based method for complex systems has a potential to be used in different AI platforms.

Automated summary

In recent years, the combination of robotic engineering and artificial intelligence technology has enriched the industry and brought it into the Industry 4.0 era. The powerful AI paradigm has the potential to revolutionize the automotive industry's outdated manufacturing strategies. To address the uncertainty of the increasingly large amounts of data encountered in these industrial sectors, a generalized IFS theory is proposed as an efficient and flexible method. Inspired by multi-attribute gamma-systems and IFS approach, a novel mathematical concept namely IF gamma R method, is developed to establish an AI platform for robotic automotive manufacturing.