Optimisation of the bending process of high strength low alloy sheet mtetal

Anchorage parts for automobile safety belts and other safety parts must resist shock loadings without breaking. They are typically made from High Strength Low Alloy sheet metal and fabricated by blanking and bending operations. The study of their behaviour during fabrication and their resulting mechanical properties has been studied experimentally and numerically. The experimental results were used to validate the numerical simulation. The resulting material damage is taken into by user subroutines in the Abaqus Standard finite element code. Damage is one of the objective functions intervening in the shape optimisation of the safety parts as well as the optimisation of the bending process. This study is based on the use of "the design of experiments technique", the approximated representation by response surfaces and the use of artificial neural networks. The objective functions represent the maximum punch loads, the maximum stresses and maximum damage during the bending operation. For unbending operations, representative of dynamic choc loadings, the objective functions are again the maximum unbending load, the maximum stresses and maximum damage. The optimisation is done by the moving least square method, evolution strategies and a global calculation method. The parameters that represent the die radius and the clearance between the sheet and tool are optimised with the objective to obtain the most resistant safety part possible.

[1]  Ridha Hambli,et al.  Comparison between Lemaitre and Gurson damage models in crack growth simulation during blanking process , 2001 .

[2]  V. G. Wong,et al.  Analysis of Stresses in Bar Cropping , 1975 .

[3]  Cv Clemens Verhoosel,et al.  Non-Linear Finite Element Analysis of Solids and Structures , 1991 .

[4]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[5]  M.S.J. Hashmi,et al.  Optimizing the laser-welded butt joints of medium carbon steel using RSM , 2005 .

[6]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[7]  Abdessamad Kobi,et al.  Application of design of experiment technique for metal blanking processes optimization , 2003 .

[8]  R. Bahloul,et al.  Experimental characterisation in sheet forming processes by using Vickers micro-hardness technique , 2006 .

[9]  Jerome Sacks,et al.  Computer Experiments for Quality Control by Parameter Design , 1990 .

[10]  Young-Hae Lee,et al.  An approach for multiple criteria simulation optimization with application to turning operation , 1996 .

[11]  Zafer Tekiner,et al.  An experimental study on the examination of springback of sheet metals with several thicknesses and properties in bending dies , 2004 .

[12]  Taylan Altan,et al.  Mathematical modeling of plane-strain bending of sheet and plate , 1993 .

[13]  P. Villon,et al.  Using the Diffuse Approximation for Optimizing the Location of Anti-Sound Sources , 1994 .

[14]  H. Gomes,et al.  COMPARISON OF RESPONSE SURFACE AND NEURAL NETWORK WITH OTHER METHODS FOR STRUCTURAL RELIABILITY ANALYSIS , 2004 .

[15]  Timothy M. Mauery,et al.  COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .

[16]  Ridha Hambli,et al.  Application of a neural network for optimum clearance prediction in sheet metal blanking processes , 2003 .

[17]  Cristina H. Amon,et al.  An engineering design methodology with multistage Bayesian surrogates and optimal sampling , 1996 .

[18]  Shih-Chieh Lin,et al.  Using neural networks to predict bending angle of sheet metal formed by laser , 2000 .

[19]  Ken R. McNaught,et al.  A comparison of experimental designs in the development of a neural network simulation metamodel , 2004, Simul. Model. Pract. Theory.

[20]  J. Lemaitre,et al.  Mécanique des matériaux solides , 1996 .

[21]  Taylan Altan,et al.  Prediction and elimination of springback in straight flanging using computer aided design methods: Part 1. Experimental investigations , 2001 .

[22]  W. L. Xu,et al.  Sensitive factors in springback simulation for sheet metal forming , 2004 .

[23]  Philip J. Withers,et al.  Prediction of damage evolution in forged aluminium metal matrix composites using a neural network approach , 1998 .

[24]  R. Bahloul,et al.  Influence de la forme des attaches de sécurité sur leur comportement en service , 2006 .

[26]  H. Huh,et al.  Design sensitivity analysis of sheet metal forming processes with a direct differentiation method , 2002 .

[27]  Branko Grizelj,et al.  Finite element approach in the plate bending process , 2002 .

[28]  Raymond L. Watrous Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization , 1988 .

[29]  Prashant P. Date,et al.  Numerical simulation of the influence of air bending tool geometry on product quality , 2004 .

[30]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[31]  Steven G. Gilmour,et al.  A Bayesian design criterion for locating the optimum point on a response surface , 2003 .

[32]  A. Delamézière,et al.  Faisabilité en emboutissage : optimisation du matériau par surface de réponse , 2002 .

[33]  Dong-Jo Park,et al.  Novel fast training algorithm for multilayer feedforward neural network , 1992 .

[34]  Ming Yang,et al.  Development of real-time process control system for precision and flexible V-bending with an on-line database , 1996 .

[35]  Akira Todoroki,et al.  Design of experiments for stacking sequence optimizations with genetic algorithm using response surface approximation , 2004 .

[36]  Eiji Nakamachi,et al.  Development of optimum process design system for sheet fabrication using response surface method , 2003 .

[37]  Sang-Moon Hwang,et al.  Finite element analysis and design in stainless steel sheet forming and its experimental comparison , 2001 .

[38]  Muhammad N. S Hadi Neural networks applications in concrete structures , 2003 .

[39]  Taylan Altan,et al.  Prediction and elimination of springback in straight flanging using computer-aided design methods: Part 2: FEM predictions and tool design , 2001 .

[40]  Jan Kusiak,et al.  Modelling of microstructure and mechanical properties of steel using the artificial neural network , 2002 .

[41]  Maurice Pillet,et al.  Les plans d'expériences par la méthode Taguchi , 2001 .

[42]  Tomas Jansson,et al.  Using the response surface methodology and the D-optimality criterion in crashworthiness related problems , 2002 .

[43]  Taylan Altan,et al.  Computer aided die design of straight flanging using approximate numerical analysis , 2003 .

[44]  J. Chung,et al.  Application of a genetic algorithm to process optimal design in non-isothermal metal forming , 1998 .

[45]  Sibylle D. Müller Bio-inspired optimization algorithms for engineering applications , 2002 .

[46]  Raphael T. Haftka,et al.  Response surface approximations for structural optimization , 1996 .

[47]  Jean-Louis Chaboche,et al.  Continuous damage mechanics — A tool to describe phenomena before crack initiation☆ , 1981 .

[48]  P. Villon,et al.  Moving least squares response surface approximation: Formulation and metal forming applications , 2005 .

[49]  G. Marron,et al.  Le pliage des aciers HLE. Prévision et maîtrise du retour élastique , 1995 .

[50]  R. Gunst Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[51]  Prashant P. Date,et al.  On the effects of geometric parameters on springback in sheets of five materials subjected to air vee bending , 2002 .