Hedi Belhadjsalah

Experimental Implementation of the Multipoint Hydroforming Process

The process of flexible hydroforming is a combination be-tween the hydroforming and the multipoint flexible forming, which allows a synergy of the advantages of two processes. On one hand, the hydroforming process allows a contribution in the flexibility by replacing one of two shaping tools by a fluid, on the other hand, the multipoint flexible forming, allows modifying freely the final shape with its reconfigurable tool, constituted by a matrices of adjustable punch elements. This innovative process presents a potential interest by accumulating at once the advantages of hydro-forming and flexible multipoint hydroforming. The purpose of this paper is to present the process review and its experimental implementation to highlight the contribution in flexibility and to validate the feasibility of the multipoint flexible hydroforming and its ability to produce of complex metal sheet part with improved quality.

A New Inverse Analysis Method for Identifying the Elastic Properties of Thin Films Considering Thickness and Substrate Effects Simultaneously

This paper presents an innovative methodology for the measurement of the mechanical properties of thin films. The purpose is to identify the elastic properties of thin film materials considering the effects of thickness and substrate simultaneously. The new approach is based on the Dimensional Analysis Method (DAM) and the Finite Element Method (FEM) to develop the model’s explicit form. Using the reverse analysis method, numerical indentation tests were carried out to obtain the Young’s modulus of the film. Therefore, the identified film modulus was completely consistent with the input modulus, which ensures the reliability and the effectiveness of the proposed method. Moreover, a case study of TiN nanocoating deposited on Zr-Based Metallic Glasses was considered to validate the proposed model. This model is useful for thin film materials and can be used in different technological applications.

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

Multiobjective Optimization of Single Point Incremental Forming: Comparison Between Isotropic and Combined Hardening Behavior

Single point incremental forming (SPIF) of sheet metal is a promising process to produce small batch production and prototyping. This process consists of a controlled process of displacement performed on a three-axis CNC milling machine. In former work, the most critical factors which affected single point incremental forming process were found to be formed shape, tool size, material type, material thickness and incremental step size. The present work is focused on an optimization strategy of (SPIF) process determined by a numerical study based on finite element analyses (FEA) according to a Box-Behnken Design of Experiments. Two types of hardening behaviour laws of material are used: isotropic and combined isotropic-kinematic hardening behaviour. To do so, a set of numerical simulations are carried out for an aluminum truncated cone as geometry of a benchmark model. The simulation results include some decisions about the mechanical resistance and geometrical quality of the parts such as the thickness distribution and the magnitude of springback. In this paper, the main objective is to present an overview of multiobjective design optimization of process parameters in single point incremental forming operation in order to minimize the sheet thinning rate and the springback simultaneously. In this investigation, the steps of optimization procedure include the using of Box-Behnken experimental design for sample producing, response surface model for coarse fitting and a developed Multiobjective Genetic Algorithm (MOGA) for exact solving of fitness functions. The results show that these methods are able to determine all the best possible compromise with respect several antagonistic objectives as well as generate the approximate Pareto optimal solutions. So these will make it possible to choose the appropriate process parameters according to the objectives functions to be minimized and consequently the improvement of the products formed by the process of incremental forming.

Multi-scale Modelling of Orthotropic Properties of Trabecular Bone in Nanoscale

The bone is a hierarchically structured material with mechanical properties depending on its architecture at all scales. In this paper, a trabecular bone multiscale model based on finite element analysis was developed to link sub-nanoscopic scale (Microfibril) and nanoscopic (Fibril) to predict the orthotropic properties of bone at different structural level. To identify the orthotropic properties, we used an inverse identification algorithm. The approach shows a good efficiency in computing.

Numerical Modeling of Hot Incremental Forming Process for Biomedical Application

The selection of a suitable technique for manufacturing of a specific product represents a complex issue, especially for the biomedical field. Single point incremental forming (SPIF) process presented an alternative manufacturing means in producing biomedical parts characterized by the need of patient-specific geometry. In addition, the titanium alloy Ti–6Al–4V is one of the most frequently used materials for biomedical application. Due to its poor-room-temperature formability, deformation under high temperature is needed. In this context, the present work aims at proving the feasibility of the hot incremental forming of titanium alloy Ti–6Al–4V denture base by finite element simulations. The effects of the forming temperature on the material failure and more particularly on the geometric accuracy of the final product were investigated. The comparison between the final deformed geometry predicted numerically and the target one obtained from CAD modeling is conducted. Therefore, a good agreement has been obtained and significant improvements in terms of geometric accuracy have been reached employing the SPIF at elevated forming temperature.

Algorithm 973: Extended Rational Fejér Quadrature Rules Based on Chebyshev Orthogonal Rational Functions

We present a numerical procedure to approximate integrals of the form ∫baf(x)dx, where f is a function with singularities close to, but outside the interval [a, b], with − ∞ ⩽ a < b ⩽ +∞. The algorithm is based on rational interpolatory Fejér quadrature rules, together with a sequence of real and/or complex conjugate poles that are given in advance. Since for n fixed in advance, the accuracy of the computed nodes and weights in the n-point rational quadrature formula strongly depends on the given sequence of poles, we propose a small number of iterations over the number of points in the rational quadrature rule, limited by the value n (instead of fixing the number of points in advance) in order to obtain the best approximation among the first n. The proposed algorithm is implemented as a Matlab program.

A Comparative Study of the Mechanical Properties of Hierarchical Trabecular Bone with other Approaches and Existing Experimental Data

The bone is a hierarchically structured material with mechanical properties depending on its architecture at all scales. Water plays an important role in the bio-mineralization process and serves as a plasticizer, enhancing the toughness of bone. In this paper, a trabecular bone multiscale model based on finite element analysis was developed to link scales from sub-nanoscopic scale (Microfibril) to sub-microscopic (Lamella) in order to predict the orthotropic properties of bone at different structural level. To identify the orthotropic properties, an inverse identification algorithm is used. Furthermore, the effect of water is incorporated. Good agreement is found between theoretical and experimental results.

Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model

This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients.