Interactive optimization tool for the optimum design of helical extension springs

This work presents an interactive computer tool for the optimal design of helical extension springs. The developed algorithm incorporates all design considerations used in the manufacturing industry and specialized design manuals. All these considerations are used to create a novel optimum design formulation. The flexibility of this software allows the user to characterize a particular design problem, through the selection of the right constraints for each case. The optimization model includes sixteen constraints (linear and nonlinear) derived from different failure criteria and designs and manufacture recommendations. This nonlinear problem is solved using sequential quadratic programming in Matlab. The characteristics of this tool are showed through development of a benchmark problem. The free software developed in this work is available at http://www.unal.edu.co/optimun/resortes/.

[1]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[2]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[3]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[4]  Xavier Llorà,et al.  Analyzing active interactive genetic algorithms using visual analytics , 2006, GECCO '06.

[5]  Robert L. Mott Machine Elements in Mechanical Design , 1985 .

[6]  Robert L. Norton,et al.  Machine Design: An Integrated Approach , 1996 .

[7]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[8]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[9]  Massimiliano Gobbi,et al.  Optimal Design of Helical Spring , 2006 .

[10]  Dirk P. Kroese,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning , 2004 .

[11]  R. T. Hinkle,et al.  Design of Helical Springs for Minimum Weight, Volume, and Length , 1959 .

[12]  I. M. Stancu-Minasian,et al.  Efficient Solution Concepts and Their Relations in Stochastic Multiobjective Programming , 2001 .

[13]  Stefan Boettcher,et al.  Extremal Optimization: an Evolutionary Local-Search Algorithm , 2002, ArXiv.

[14]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[15]  Mitsuo Gen,et al.  A solution method for optimal weight design problem of herical spring using genetic algorithms , 1997 .

[16]  Joseph Edward Shigley,et al.  Mechanical engineering design , 1972 .

[17]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[18]  Manuel Paredes,et al.  Obtaining an optimal compression spring design directly from a user specification , 2002 .

[19]  Sadiq M. Sait,et al.  Evolutionary algorithms, simulated annealing, and Tabu search: a comparative study , 1998, Optics & Photonics.

[20]  Manuel Paredes,et al.  An optimization process for extension spring design , 2001 .