Abstract
Aims: In this study, we conducted a bibliometric study about steel casting between the year 2000-2023. We carried out a bibliometric analysis of sand casting, investment casting, die casting, and squeeze casting in which optimization and simulation models are available and have been thoroughly developed to enhance the quality of the casting product, according to the keyword co-occurrence network and word cloud generated by the bibliometric analysis and text mining of the publications.
Methods: By delving further into the optimisation and simulation models, this study finds multiple casting procedures with various process parameters that have a major effect on the process results. Defects of the mechanical kind are the most prevalent, and factors taken into consideration are emissions, yield, dimensional tolerances, and qualities.
Results: The necessity for data-driven modelling in new casting environments has been identified in this study, which will allow for a dynamic casting process and fine-tuning and aid in attaining desirable results in today's competitive markets. In order to illustrate the future prospects of this sector, this research focuses on potential technical interventions in steel casting processes that could enhance the efficiency of the process and the quality of the products produced by steel casting.
Conclusion: This study examines the body of literature on various researchers' contributions to the production of excellent casting components and performs a bibliometric examination of the publications. However, the literature study examines research publications from high-quality essential sources to determine the essential criteria influencing steel casting quality.
