Numerical Modelling and Simulation of Sheet Metal Forming Process

Simulation and modelling of sheet metal forming process are well common today in different industries (automotive, aerospace) and several research centers regarding its huge impact for both on production and reliability of the lifecycle of the equipment, and the quality of the product. However, to obtain the best configuration possible with the inputs parameters to achieve high level of production and increasing the durability of the tools needs some extra methods for the optimization for this problem using mostly finite element method cooperated with iterative algorithms based on Artificial Neural Network (ANN) [1]. Whereas this research is focused on modelling of stamping process of stainless steel AISI 304 to investigate to formability of the material, and studying the influence of the friction factor on the quality of the product as well the energy required for each set configuration.
 


Introduction
Sheet metal forming processes are widely used and common in our days from the automotive industries to the aerospace and home appliance application regarding the huge benefits and income of its application. Hence, we need forming parts with high complexity forms, less energy, then these kind of forming processes is highly recommended [1].
Whereas there are a lot of advanced research and methods was elaborated to deal with field of sheet metal forming process. There are always new techniques and optimization strategies needed to optimize some input parameters during the initial configuration in order to overcome the unusual results both on our final product and increase the durability of tools and equipment, and the efficiency of the process design as well.
Whereas this research is focused on modelling of stamping process of stainless steel AISI 304 to investigate to formability of the material, and studying the influence of the friction factor on the quality of the product as well the energy required. Some an overview about the optimization techniques in this field of research will be given in the first part.

Sheet metal forming processes optimization techniques
Optimization of sheet metal forming processes are evolving faster and longer in our days. For instance, the numerical simulation is well known for several purpose, there is missing gaps related to optimization using others methods based on artificial intelligence algorithm such Neural Network Algorithm.
As one of basic method for metal plastic processing, with the development of finite element technology, using numerical simulation we can accurately predict the results of forming process virtually, but in the optimization of forming process parameters, still mainly use the numerical method combined with trial and error technique to do so. But in order to select the optimal set configuration possible involved more combinations of design variables, it is so difficult and time, tests consumed by experience. So, it is mandatory to use artificial neural network, orthogonal experiment, and others methods for the sheet metal forming parameters optimization [2].

Artificial Neural Network
Artificial Neural Network (ANN) is a complex network system, which is composed of a large number of simultaneous and very simple processing units (or neurons). This method is especially suitable for processing need to consider many factors and conditions, inaccurate and fuzzy information problems [3].

Numerical modelling of stamping process of stainless steel AISI 304
This research is focused on modelling of stamping process of stainless steel AISI 304 to investigate the formability of the material, and to study the influence of the friction factor on the quality of the product

Material behaviour
The blank is made of stainless steel AISI 304. It is an austenitic stainless steel composed of at least 18% chromium and 8% of nickel. It has the properties of being weldable and ductile. Its Young's modulus is = 200 GPa and its Poisson's ratio is = 0.29 Several hardening models have been tested and Ludwick's hardening law seems the best suited to describe the behaviour of AISI 304 by linking the flow stress to the plastic deformation [4]. This law is written in the form: Where σ e , and are intrinsic characteristic related to material, they have the values defined in the table 1.   So, it is recommended to mesh the deformable body with shell element.
 Whereas the tools are meshed as discrete rigid body, the blank is meshed with triangular thin shell element.
 The interaction between the dies and the deformable with friction, using the value = 0.15  The stress is high in the three main regions in the deformed body denoted by Zone 1, Zone 2, and Zone 3. However, the maximum value is located in zone 2 ( = 888.5 MPa ).

 Thickness distribution
One of the main useful curves and results is to plot the thickness in the deformed body and make comparison with some predefined equation and experimental tests with the real thickness change. In the Figure 5 has shown the value of the thickness taken at in symmetry plane. The range of thickness between 1.5 to 2.07 mm in the fork. However, the minimum value is reached in Z1 with = 1.06 mm  Energy required In order to identify la force nominal required to form this hardening material is to plot la reaction force as function of displacement in the punch as shown in the Figure 6 illustrated below.