Considering that the challenges of manufacturing demands are becoming increasingly more complicated, particularly in aviation machining, decision-making regarding the choice of the most appropriate process parameters remains a daunting task. This work primarily aims at exploring how Knowledge Graphs and Retrieval-Augmented Generation can be used as a suggested architecture of intelligent and explainable decision-making systems. The literature analysis was through content analysis of recent refereed journal articles as a technique of secondary research methodology applied in the study to determine the existence of technological complementarities as well as identify gaps in research. The findings confirm the fact that the KGs enhance the context and audit trails whereas RAG provides the dynamic and context-based searches of information that have a positive influence on the machining decisions. This integration will surely be of great help since it would reduce the trial and error, improve accuracy, and sustainability of critical applications. This work can make a contribution through the combination of semantic technologies and generative AI in making decisions in the manufacture industry of aircraft. This piece of work is applicable to the future development of smart and dynamic supporting systems in line with the technological requirements of Industry 4.0.
