Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also...

[1]  D. Zheng,et al.  Prediction of Tire Tread Wear with FEM Steady State Rolling Contact Simulation , 2003 .

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

[3]  T. Kamegawa,et al.  Theory of Optimum Tire Contour and Its Application , 1996 .

[4]  Tomoyuki Hiroyasu,et al.  NCGA: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems , 2002, GECCO Late Breaking Papers.

[5]  Yukio Nakajima,et al.  Hydroplaning Analysis by FEM and FVM: Effect of Tire Rolling and Tire Pattern on Hydroplaning , 2000 .

[6]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[7]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[8]  Qiang Yu,et al.  Structural optimization using the design of experiments and mathematical programming , 1996 .

[9]  Y. Nakajima,et al.  Optimum Young's Modulus Distribution in Tire Design , 1996 .

[10]  H. Heguri,et al.  Optimization for Motorcycle Tire Using Explicit FEM , 2001 .

[11]  Shigeru Obayashi,et al.  Self-organizing map of Pareto solutions obtained from multiobjective supersonic wing design , 2002 .

[12]  Y. Nakajima,et al.  Surface Shape Optimization of Tire Pattern by Optimality Criteria , 2003 .

[13]  Shigeru Obayashi,et al.  Niching and Elitist Models for MOGAs , 1998, PPSN.

[14]  R. KrishnaKumar,et al.  Application of a Shell‐Spring Model for the Optimization of Radial Tire Contour Using a Genetic Algorithm , 2003 .

[15]  T. Kamegawa,et al.  Application of a Neural Network for the Optimization of Tire Design , 1999 .

[16]  Sanjay Govindjee,et al.  Inverse Design Methodology of a Tire , 2001 .