Nombre total de résultats : 2
Pertinence Titre A-Z Plus récents Plus anciens
10 25 50
Année de publication

Optimizing MAG Welding Input Variables to Maximize Penetration Depth Using Particle Swarm Optimization Algorithm

Mohamed MEZAACHE, Omar Fethi BENAOUDA, Saad CHAOUCH, Badreddine BABES, Rachid AMRAOUI  (2021)
Article de conférence

Systems based on artificial intelligence, such as particle swarm optimization and geneticalgorithm have received increased attention in many research areas. One of the main objectives inthe gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as acharacteristic of quality and stiffness. This article has examined the application of particle swarmoptimization algorithm to obtain a better DP in a GMAW and compare the results obtained with thetechnique of genetic algorithms. The effect of four main welding variables in GMAW process whichare the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distanceon the DP have been studied. For the implementation of optimization, a source code has beendeveloped in MATLAB 8.3. The results showed that, in order to obtain the upper penetration depth,it is necessary that: the welding voltage, the welding speed and the nozzle-to-plate distance must beat their lowest levels; the wire feed speed at its highest level Voir les détails

Mots clés : Artificial intelligence, Particle Swarm Optimization, Genetic algorithm, GMAW, penetration depth, optimization, Matlab

Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model

Rabah ABDELKADER, Naim Ramou, Mohammed Khorchef, Nabil CHETIH, Yamina BOUTICHE  (2021)

The welding defects detection in industries is becoming an important area and is attracting the attention of many researchers. Radiography is one of the most widely used techniques for inspecting weld defects. X-ray images are generally characterized by low contrast, poor quality and uneven illumination, so the extraction of weld defects could become a difficult task. Among the techniques most used in this field, it is the active contour and the main problem of this technique is the initial contour selection. To solve this problem and obtain reliable and efficient detection of welding defects, we propose in this work a new approach for welding defects detection from x-ray image based on an improved Chan-Vese model. This improved model is based on three stages. The first stage is the detection the region of interest. In the second stage, we apply the Fuzzy C-Mean (FCM) algorithm to select one of the clusters as the initial contour. In the third stage, we use the Chan-Vese model and the selected initial contour to segment the acquired images and obtain the boundaries of the weld defects. Experiments are carried out on different x-ray welding images of the GDxray database in order to extract the characteristics of the welding defects. The results obtained show the effectiveness of the proposed approach compared to conventional techniques. Voir les détails

Mots clés : Chan-Vese model Fuzzy, C-means clustering, X-ray image, Welding defects