NAFEMS World Congress (NWC) is the world's largest and only independent international conference dedicated to simulation. Keyword Network Analysis(KNA) was performed on 1,554 papers presented at NWC over the past 10 years, a total of 5 conferences have been held. The network is built and visualized as nodes and edges. Nodes are created by selecting only meaningful words from the titles of published papers, edges are created from relations between nodes. It was found that the top 8 clusters contained a total of 87% of the data in the network. In order to explain the importance of the nodes of the network, four centrality indices were used. That is degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. Among the node commonly included in each centrality, meaningful words include optimization, system, process, data, and composite. Since the NWC covers a wide range of topics related to simulations, specific subject areas should be targeted for further exploration. In this study, two topics of Additive Manufacturing (AM) and AI & Machine Learning (AI/ML) were used as examples. AM sessions have been running since 2015, and a total of 70 papers are covered in this network analysis. Comparing the four centrality values of the network, it can be seen that there is a lot of research on the printing of metallic materials and a lot of interest in topology optimization. Fatigue and life scored high in eigenvector centrality. This explains that these words are linked to high-impact words. AI/ML sessions have been running since 2019, but there are several cases in which AI/ML related techniques are included in other sessions as well. If AI-related words are in the title, they are included as subjects and a total of 33 papers were analyzed. Currently, AI technology is being applied to CFD and crash, and research on Generative Adversarial Network (GAN) and Physics Informed Neural Network (PINN) technologies is also being attempted. A detailed analyses of AM and AIML include networks by meaningful words, relations among authors, and network changes over time. KNA analysis is considered a good methodology to identify the growth and change of technology because it can identify keyword relationships, research group networks, and key researchers.