Publications Scientifiques
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Item Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network(Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel WahabLocal stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoringItem Efficiency of bio- and socio-inspired optimization algorithms for axial turbomachinery design(Elsevier, 2017) Ait Chikh, Mohamed Abdessamad; Belaidi, Idir; Khelladi, Sofiane; Paris, José; Deligant, Michael; Bakir, FaridItem Genetic algorithm based objective functions comparative study for damage detection and localization in beam structures(Institute of Physics Publishing, 2015) Khatir, Samir; Belaidi, Idir; Serra, R.; Benaissa, Brahim; Ait Saada, AichaThe detection techniques based on non-destructive testing (NDT) defects are preferable because of their low cost and operational aspects related to the use of the analyzed structure. In this study, we used the genetic algorithm (GA) for detecting and locating damage. The finite element was used for diagnostic beams. Different structures considered may incur damage to be modelled by a loss of rigidity supposed to represent a defect in the structure element. Identification of damage is formulated as an optimization problem using three objective functions (change of natural frequencies, Modal Assurance Criterion MAC and MAC natural frequency). The results show that the best objective function is based on the natural frequency and MAC while the method of the genetic algorithm present its efficiencies in indicating and quantifying multiple damage with great accuracy. Three defects have been created to enhance damage depending on the elements 2, 5 and 8 with a percentage allocation of 50% in the beam structure which has been discretized into 10 elements. Finally the defect with noise was introduced to test the stability of the method against uncertaintyItem 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 solutionsItem Analysis of modular fixtures design(Mecanique et Industries, 2007) Zirmi, S.; Paris, H.; Belaidi, IdirA fixture is essential equipment to firmly fix the part at the good position in the workspace of the machine tool. The design of the fixture plays an important part to obtain a machined part of good quality. We already formalized knowledge on the fixturing so as to be able to manage them throughout process planning design. Now we are interested in the design of the fixture so as to pass from the fixturing model to the technological solution which will be used on the machine tool. This paper deals with the problem of modular fixture design with a technological point of view. The choice and the placement of element are carried out in taking account of all the constraints: quality, accessibility and mechanical behaviour of the system part fixture cutting tool during the machining.