Prediction of optimum clearance in sheet metal blanking processes

Abstract

The blanking process and structure of the blanked surface are influenced by both the tooling (clearance and tool geometry) and properties of the workpiece material (blank thickness, mechanical properties, microstructure, etc.). Therefore, for a given material, the clearance and tool geometry are the most important parameters. The objective of the present work is to develop a methodology to obtain the optimum punch-die clearance for a given sheet material by simulation of the blanking process. A damage model of the Lemaitre type is used in order to describe crack initiation and propagation into the sheet. A comparative study between numerical and experimental results shows good agreement.

The Effect of the Thickness-to-Die Diameter Ratio on the Sheet Metal Blanking Process

“…This FEM code is based on an implicit Lagrangian computational routine [18]. The blanking process was considered axisymmetric, and a 2D model was used to simulate the process parameters.…”

“…proposed using a FEM method and neural network analysis to optimize the clearance between punch and die [17] and [18]. Mole et al [19] proposed a new FEM method to optimize the blank shape.…”

An Application of Response Surface Method in the Optimization of Sheet Metal Blanking Process

“…Through literature, it is clear that many methods are used to study the blanking process to achieve the optimal combination of its parameters. This includes analytical approaches [6,7]; Finite Element Method [8, 9, and 10]; Design of Experiments [1,10] and Neural Networks Modeling [11]. Literature shows that the mechanical characteristics of the blanking process and the geometrical aspect of the sheared edge are affected by different parameters.…”

The Best Location for Speed Bump Installation Using Experimental Design Methodology

“…Some of the previous experiments have been done by experimenters to formulate certain hypotheses about a particular process. Hambli et al [15] found that the design of experiment technique is an efficient and cost-effective way to model and analyze the interactions which indicate process variations. DOE also has wide application in the improvement of new processes in such a way that processes can be defined based on several controllable variables.…”