Optimization of Spot-Welded Joints Combined Artificial Bee Colony Algorithm with Sequential Kriging Optimization

Generally, spot-welded joints are the weakest parts of structures leading to fatigue failure under fluctuating loads. Therefore, it is important to optimize the spot weld to improve the fatigue life. However, a classical optimization of the spot weld often directly couples finite element analysis (FEA) with optimization algorithm, which may fall into a local optimum or be expensive computationally. In this study, a metamodel-based optimization procedure is proposed to find the optimum locations of spotwelded joints for maximum fatigue life. Based on the initial training points, Kriging model is implemented to approximate the objective function regarding the design variables (i.e., locations of spot welds). To further overcome the defect of traditional Kriging model and improve the accuracy of optimumresults, the sequential Kriging optimization (SKO) is utilized, where theKrigingmodel is updated iteratively by adding new training points to the training dataset till the global optimum is obtained. The optimization is run using artificial bee colony (ABC) algorithm and the results show that our proposed method is able to improve the performance of the spot-welded joint. More importantly, more competent optimum can be found and the optimization can be executed more efficiently, compared to the conventional methods.

[1]  Qing Li,et al.  Parametric analysis and multiobjective optimization for functionally graded foam-filled thin-wall tube under lateral impact , 2014 .

[2]  Magnus Arnér,et al.  Design and Analysis of Computer Experiments , 2014 .

[3]  Yunkai Gao,et al.  Multiobjective reliability-based optimization for design of a vehicledoor , 2013 .

[4]  Dervis Karaboga,et al.  Artificial bee colony programming for symbolic regression , 2012, Inf. Sci..

[5]  Guangyao Li,et al.  Variable fidelity design based surrogate and artificial bee colony algorithm for sheet metal forming process , 2012 .

[6]  N. Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[7]  K. Yamazaki,et al.  Sequential Approximate Optimization using Radial Basis Function network for engineering optimization , 2011 .

[8]  Ahmet H. Ertas,et al.  Design optimization of spot-welded plates for maximum fatigue life , 2011 .

[9]  Mustafa Sonmez,et al.  Artificial Bee Colony algorithm for optimization of truss structures , 2011, Appl. Soft Comput..

[10]  Lionel Fourment,et al.  Optimization of forging processes using Finite Element simulations , 2010 .

[11]  Kwon-Hee Lee,et al.  Metamodel-based optimization of a control arm considering strength and durability performance , 2010, Comput. Math. Appl..

[12]  Dervis Karaboga,et al.  Artificial bee colony algorithm , 2010, Scholarpedia.

[13]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[14]  Wei Chen,et al.  Optimizing Latin hypercube design for sequential sampling of computer experiments , 2009 .

[15]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[16]  Ahmet H. Ertas,et al.  Optimization of spot-weld joints , 2009 .

[17]  Ruijie Wang,et al.  Low-cycle fatigue life prediction of spot welds based on hardness distribution and finite element analysis , 2009 .

[18]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[19]  Kaushik Sinha,et al.  Reliability-based multiobjective optimization for automotive crashworthiness and occupant safety , 2007 .

[20]  R. A. Miller,et al.  Sequential kriging optimization using multiple-fidelity evaluations , 2006 .

[21]  N. Zheng,et al.  Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..

[22]  Wei Chen,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[23]  Sankaran Mahadevan,et al.  Damage tolerance reliability analysis of automotive spot-welded joints , 2003, Reliab. Eng. Syst. Saf..

[24]  Ning Pan,et al.  Spot welds fatigue life prediction with cyclic strain range , 2002 .

[25]  Soo Won Chae,et al.  An optimal design system for spot welding locations , 2002 .

[26]  M. Sasena,et al.  Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization , 2002 .

[27]  David Taylor,et al.  Optimization of spot-welded structures , 2001 .

[28]  Ren-Jye Yang,et al.  Metamodeling development for vehicle frontal impact simulation , 2001, DAC 2001.

[29]  Wei Chen,et al.  Three-dimensional finite element analysis of the mechanical behavior of spot welds , 2000 .

[30]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[31]  F. Ellyin,et al.  High-Strain Multiaxial Fatigue , 1982 .

[32]  Yunkai Gao,et al.  Multi–disciplinary optimisation for front auto body based on multiple optimisation methods , 2011 .

[33]  Roberto Tovo,et al.  A numerical approach to fatigue assessment of spot weld joints , 2011 .

[34]  S. Rahman.  Reliability Engineering and System Safety , 2011 .

[35]  Ahmet H. Ertas,et al.  Measurement and Assessment of Fatigue Life of Spot-Weld Joints , 2009 .

[36]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[37]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[38]  Michael James Sasena,et al.  Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. , 2002 .

[39]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .