there are many parameters that affect the sheet hydroforming process such as fluid pressure, material properties, interfacial friction between blank and tool surfaces, etc. in this paper, a fem-based taguchi method is used to determine the effects of forming parameters on the quality of part formability in the process of hydrodynamic deep drawing assisted by radial pressure. four important form...

In this work, an inverse finite element formulation was modified for considering material anisotropy in obtaining blank shape and forming severity of deep drawn orthotropic parts. In this procedure, geometry of final part and thickness of initial blank sheet were known. After applying ideal forming formulations between material points of initial blank and final shape, an equation system was obt...

the aim of this paper is to develop a systematic method to analyze the effects of forming parameters on the quality of part formability and determine the optimal combination of the forming parameters for the sheet hydroforming process. in this paper, the effects of four important process parameters namely fluid pressure, friction coefficient at blank/punch interface, gap between die rim block a...

Thin glass sheets may be manufactured using a two-part process in which a sheet is first cast and then subsequently reheated and drawn to a required thickness. The latter redrawing process typically results in a sheet with non-uniform thickness and with smaller width than the cast glass block. Experiments suggest that the loss of width can be minimized and the non-uniformities can be essentiall...

In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the...

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent ...