Optimizing the Parameters Contributing to Riveting Quality Using Imperialist Competitive Algorithm and Predicting the Objective Function via the Three Models MLR, RBF, and ANN-GA

The metal sheets play an important role in the mechanical design, particularly in the aerospace structures. The rivet connections are frequently used to connect these sheets. The riveting quality greatly influences the rupture of the rivet and the sheet. The various parameters affect the quality of this operation. In this paper, the optimization of the parameters contributing to the riveting quality in order to minimize the value of the maximum tangential stress in the sheets is addressed. To this end, the tolerance of the hole diameter in the top and the bottom sheets, the friction coefficient, and the tolerance of the rivet diameter and the rivet length were considered as the parameters influencing the riveting quality. A total of 64 models were obtained by the permutations of the parameters two at a time. The outputs were determined using the finite element method. The objective function for the optimization is the maximum tangential stress for which there is no analytical relation. Thus, three methods including the multivariable linear regression (MLR), the artificial neural network model of the radial basis function (RBF) type, and the hybrid model of the artificial neural network and the genetic algorithm (ANN-GA) were employed to model this function. Further, the performance of the three models was compared and the most suitable one was selected to model the objective function. The regression model was used to model the values of the height and the diameter after riveting. The imperialist competitive algorithm is utilized to solve this optimization problem. The obtained value for the maximum tangential stress using the imperialist competitive algorithm is 16368 pounds per square inches. After modification, this value increased to 23440 pounds per square inches using the finite element method. . The 0.07689 inches and 0.18524 inches were obtained for the height and diameter of the rivet after riveting, respectively.

[1]  E. Fernández,et al.  Finding Optimal Neural Network Architecture Using Genetic Algorithms , 2007 .

[2]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..

[3]  L. W. Reithmaier Standard Aircraft Handbook for Mechanics and Technicians , 2013 .

[4]  Kazuyuki Aihara,et al.  Adaptive annealing for chaotic optimization , 1996 .

[5]  John W. Hutchinson,et al.  The clamping stress in a cold-driven rivet , 1998 .

[6]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[7]  A. Daidie,et al.  Study and numerical characterisation of a riveting process , 2008 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Haralambos Sarimveis,et al.  A line up evolutionary algorithm for solving nonlinear constrained optimization problems , 2005, Comput. Oper. Res..

[10]  Eric Markiewicz,et al.  Riveted joint modeling for numerical analysis of airframe crashworthiness , 2001 .

[11]  Thomas Farris,et al.  Linking Riveting Process Parameters to the Fatigue Performance of Riveted Aircraft Structures , 2000 .

[12]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[13]  R. Müller An experimental and analytical investigatin on the fatigue behaviour of fuselage riveted lap joints , 1995 .

[14]  P. Angeline Evolving fractal movies , 1996 .

[15]  S. Hossein Cheraghi,et al.  Effect of variations in the riveting process on the quality of riveted joints , 2008 .

[16]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[18]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[19]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[20]  Jerome B. Cohen,et al.  Residual stresses in and around rivets in clad aluminum alloy plates , 1994 .

[21]  T. Ryzhova,et al.  Estimation of the reliability of ultrasonic quality control of riveted joints with clearance , 1995 .

[22]  M. F. Cardoso,et al.  A simulated annealing approach to the solution of minlp problems , 1997 .

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[25]  W. S. Johnson,et al.  Effect of Interference on the Mechanics of Load Transfer in Aircraft Fuselage Lap Joints , 2007 .

[26]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[27]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.