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      The Prediction of Laser-Arc Hybrid Welding Bead Shape Basing On Multiple Population Genetic Algorithm and Neural Networks

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          Abstract

          Abstract In hybrid welding, due to the large number of welding parameters and the coupling between different welding parameters, any change of welding parameters will have a significant impact on the weld section size, so it is always essential to choose reasonable welding parameters to improve the stability of the weld. In this paper, an automatic measurement system for weld section size is designed. The center point of the weld section contour is taken as the origin of the polar coordinate system, and the pixel coordinates of the boundary points on the weld section contour are detected every 15 degrees. Based on 32 groups of laser-arc hybrid welding experiments, the BP (Back Propagation) neural network is used to establish the prediction model between the input parameters (welding current, laser power, welding Angle, welding gap, and weld blunt) and the output weld section size. Aiming to the problem of too many input and output parameters of the BP neural network, a multiple population genetic algorithm (MPGA) is introduced to optimize the internal weights and thresholds of the BP neural network to improve the prediction accuracy. Finally, the difference between the dimensions of the left and right sides of the weld is calculated as the symmetry of the weld profile. The results show that the measurement accuracy of the automatic measurement system can reach 98%, and the prediction accuracy of the section size and symmetry can reach about 90% with the optimized BP neural network. The research method in this paper is of great significance to the study of welding process parameter optimization.

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          Artificial Neural Networks Based Optimization Techniques: A Review

          In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.
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            Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection

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              Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem

              The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic high-speed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.
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                Author and article information

                Journal
                si
                Soldagem & Inspeção
                Soldag. insp.
                Associação Brasileira de Soldagem (São Paulo, SP, Brazil )
                0104-9224
                1980-6973
                2024
                : 29
                : e2912
                Affiliations
                [01] Jiangmen orgnameJiangmen Polytechnic orgdiv1College of Intelligent Manufacturing and Equipment orgdiv2Department of Mechantronics China
                [02] Batangas orgnameBatangas State University orgdiv1The National Engineering University orgdiv2College of Engineering Philippines
                Author information
                https://orcid.org/0000-0001-8714-5832
                https://orcid.org/0000-0002-6280-6259
                https://orcid.org/0009-0007-6793-7382
                https://orcid.org/0009-0008-1776-576X
                https://orcid.org/0009-0000-0898-9333
                Article
                S0104-92242024000100903 S0104-9224(24)02900000903
                10.1590/0104-9224/si29.12
                2aeefd4a-33b7-4303-b302-05aa1ad0f11b

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 11 April 2024
                : 02 September 2024
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 15, Pages: 0
                Product

                SciELO Brazil


                Laser-arc hybrid welding,Neural network,Genetic algorithm,Value of symmetry

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