Tube end closing is a metal forming process that replaces welding processes while closing tubes ends. It depends on deforming a rotating tube using a roller, and therefore, it is also called tube end spinning. The process involves many parameters like contact depth, roller inclination angle, roller diameter, mandrel curvature, and tube rotational speed. This study develops a finite element model (FE-model) for this process and validates it through experimental results. The numerical and experimental results have shown minor deviation of 1.87%. The FE-model is then employed to carry out a statistical analysis based on the response surface method (RSM). The analysis of variance (ANOVA) and regression analysis have proved the accuracy of the obtained mathematical model. The contact depth has proved to have the most significant effect in the process responses, while the roller diameter has the least effect. Finally, an optimization analysis is carried out to select the finest conditions for the process.
This paper presents an integrated simulation system that is employed in order to evaluate the static and dynamic performance of a milling machine. The paper discusses the design consideration of the evaluation system, creates the system based on finite element technique, applies it to a case study, and discusses the results. Obtaining such a reliable model could replace many experimental tests that must otherwise be carried out each time the parameters affecting cutting conditions are changed. Modeling and meshing of various machine elements including the mechanical structure are carried out, contacts between each adjacent elements are defined, load components generated from machining process are modeled, and finally the static and dynamic performance of the entire machine is evaluated. The machine performance is identified in terms of static loop stiffness in both x and y directions, mode shapes, and frequency response function at tool center point.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.