Browsing by Author "Sahali, M. A."

Now showing 1 - 3 of 3

Crack growth estimation using model reduction and genetic algorithm
(· · · · ·, 2014) Benaissa, B.; Belaidi, Idir; Khatir, S.; Hamrani, A.; Lessoued, M.; Sahali, M. A.
In this study we use the material elastic properties as a base, a tow dimensional cracked plate under traction is modelled by finite element method (FEM) than a reduced model is built using the proper orthogonal decomposition method (POD), the crack length is estimated as an inverse identification problem, basing on the deformation obtained from the boundary nodes of the structure considered as sensor points. A genetic algorithm (GA) is used for the minimization of the error function which is expressed as the difference between displacement field of the boundaries caused by the crack size proposed randomly by GA and the field measured at the actual identity. The approach presented accurate results and could guess the real crack size in a precession less that 10-6 of the cost function, proving its effectiveness even with a very low number of 4 sensors, and shows that the boundary displacement measurements are practical. The use of the reduced model provides tangible benefits mainly the very low computational cost
Efficient genetic algorithm for multi-objective robust optimization of machining parameters with taking into account uncertainties
(Springer, 2014) Sahali, M. A.; Belaidi, Idir; Serra, R.
The respect of the machined piece quality and productivity is closely related to the mastery of uncertain factors. Indeed, the efficient solutions obtained from the machining parameter optimization based on classical methods are assigned of uncertain deviations which affect the cutting process. In the present paper, we propose multi- and mono-objective optimization approach of parameter turning with taking into account both production constraints related to piece quality, to machine power, or to tool life, than uncertainty factors related to the tool wear and to piece geometry defaults. To this end, we developed and implemented an efficient genetic algorithm, based on an evaluation mechanism of “objective” functions, which integrate the Monte Carlo simulations to calculate the robustness of objective function and different constraints. Our approach has been validated by two applications implemented with Matlab™ for the minimization of cost and machining time, which has allowed obtaining simultaneously efficient and robust results and offering the possibility to choose beforehand a compromise between efficiency and robustness of solutions
New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm
(Springer, 2015) Sahali, M. A.; Belaidi, Idir; Serra, R.
In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimiza- tion problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumula- tive distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘ closed ’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technolog- ical limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing op- eration; therefore, the economic objectives are minimized un- der imposed maximum probabilities of failure. In both situa- tions, the efficiency and robustness of optimal solutions