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Experimental and numerical investigation on ductile fracture behavior of welded areas under tensile and shear forces

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ENGINEERING STRUCTURES 2025年 329卷

作者： Wang, Yifei Tong, Lewei Shi, Weizhou Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China Shanghai Inst Architectural Design & Res Co Ltd Shanghai 200041 Peoples R China Shanghai Engn Res Ctr Assembly Bldg Technol Integr Shanghai 200011 Peoples R China

Due to complex stress conditions, welded joints in steel and concrete-filled steel tubular trusses are considered weak parts and are susceptible to failure by fracture. To investigate the ductile fracture behavior of steel tubular welded joints under a combination of tension and shear, this study took 36 specimens from the base/tube metal (BM), heat-affected zone (HAZ) and weld metal (WM) in welded areas. The fracture behavior of five kinds of specimens were compared. Then, to simulate fracture patterns of specimens, this study developed a new Shear- Modified GTN model (SMGTN), which was based on two types of damage variables: tension and shear. The particle swarm optimization (pso) algorithm was employed to calibrate SMGTN model parameters. Finally, the stress conditions, damage, and fracture patterns of various specimens were analyzed. The result demonstrated that the larger the radius or shear center rotation angle of notched specimens, the larger the fracture displacement. The materials in BM, HAZ, and WM regions exhibited different ductile behavior under different stress conditions. The calibrated parameters of SMGTN model in this study accurately predicted the fracture displacement and development patterns in the BM, HAZ, and WM regions.

关键词： Q355 BM-HZA-WM Tension Shear And combined stress conditions Shear-modified GTN model pso algorithm Damage evolution and fracture prediction

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Optimum Online Controller Tuning Through DE and pso algorithms: Comparative Study with BLDC Motor and Offline Controller Tuning Strategy 10

Optimum Online Controller Tuning Through DE and PSO Algorith...

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10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Rojas-Lopez, Alam Gabriel Villarreal-Cervantes, Miguel Gabriel Rodriguez-Molina, Alejandro Inst Politecn Nacl CIDETEC Postgrad Dept Mexico City 07700 DF Mexico Tecnol Nacl Mexico IT Tlalnepantla Res & Postgrad Div Tlalnepantla De Baz 54070 Mexico

ISBN: (纸本)9798350359374

This work presents the online controller tuning, through metaheuristic algorithms, for a Proportional Integral controller of a Brushless Direct Current (BLDC) motor via the indirect adaptive tuning approach. The tuning is an optimization problem solved through Differential Evolution (DE) and Particle Swarm Optimization (pso) algorithms in their online versions. The functionals of the optimization problem aim to minimize the Integrated Absolute Error (IAE) about speed regulation and reduce the Average Power required by the motor. A set of experiments considering different disturbances applied to the motor is performed, and a comparison between the optimal online and the optimal offline approaches is carried out. Finally, the results show that the online approach improves speed regulation and reduces the motor's energy consumption.

关键词： Online control tuning Indirect adaptive tuning Metaheuristics pso algorithm DE algorithm Brushless motor

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Implementation of Real Coded pso algorithms using FPGA Technology 15

Implementation of Real Coded PSO Algorithms using FPGA Techn...

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15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

作者： Ben Ameur, Mohamed Sadok Sakly, Anis Mtibaa, Abdellatif Univ Monastir Lab Elect & Microelect Monastir Tunisia Univ Monastir Natl Engn Sch Monastir Monastir Tunisia

ISBN: (纸本)9781479959075

In this paper, a parallel pso algorithm structure based on (FSM) Finite state machine is proposed. The use of FPGA (Field Programmable Gate Array) has shown many advantages over other circuits based on swarm intelligence mainly due to its simplicity and robustness to solve mathematic problems that contain several variables. This paper evaluates the performance of the pso that's why we developed a hardware architecture that accelerates its execution performance by updating velocity and position of particles. The fitness evaluation module is implemented and mapped into FPGA reconfigurable hardware. The accelerated execution performance of the proposed architecture is demonstrated on standard mathematical functions as well as on a real coded problem.

关键词： pso algorithm FSM FPGA

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Error optimization using Bat and pso algorithms for machine vision system based tool movement 3

Error optimization using Bat and PSO algorithms for machine ...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Garg, Anu Mahapatra, Prasant Kumar Kumar, Amod Cent Sci Instruments Org CSIR Biomed Instrumentat Div Chandigarh India Guru Jambheshwar Univ Sci & Technol Dept Opt Engn Appl Phys Hisar Haryana India

ISBN: (纸本)9781479930807

This paper presents a comparison of Bat and Particle Swarm Optimization (pso) algorithms for optimization of lathe tool positional error in a developed machine vision system for determination of lathe tool position and verification. Bat algorithm is based on echolocation behavior of bats while pso is inspired by social behavior of birds flocking in search for food. Both metaheuristic algorithms were tested on lathe tool movement ranging from 0.020 mm to 7 mm. The results for various lathe tool movements have demonstrated that Bat algorithm outperforms the pso algorithm.

关键词： Bat algorithm pso algorithm Error optimization Bio-inspired algorithms Machine Vision System

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Multi-stage adaptive speed control with torque ripple optimization for a switched reluctance motor in electric vehicle applications

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RESULTS IN ENGINEERING 2025年 25卷

作者： Boumaalif, Youness Ouadi, Hamid Giri, Fouad Mohammed V Univ ENSAM RABAT Rabat 6207 Morocco Univ Caen Normandy Dept Elect Engn F-14032 Caen France

This work addresses the problem of speed control for switched reluctance motors (SRM) used in electric vehicles, with a focus on minimizing torque ripple by optimizing the current reference shape. This issue is formulated as a constrained optimization problem, particularly difficult due to the nonlinear and nonconvex relationship between currents and torque. In this context, a two-stage optimization strategy is proposed. Using the particle swarm optimization (pso) technique, the first stage computes in offline mode, a first estimate of the optimal parameters of the SRM current reference signal. The second stage refines, in real time, this estimate using the extremum seeking (ES) technique. The speed controller is designed with the backstepping approach based on a SRM nonlinear model taking into account the magnetic saturation phenomenon of this machine. This ensures good control performance across a wide operating range. The effectiveness of the proposed control strategy is demonstrated through simulations conducted in MATLAB-Simulink. Results indicate that the proposed strategy achieves a 48% reduction in torque ripple compared to a non-adaptive controller and a 12% reduction compared to a strategy based on single-stage optimization performed in offline mode.

关键词： Multi-stage adaptive speed control Switched reluctance motor (SRM) Electric vehicle pso algorithm Extremum seeking (ES) Backstepping nonlinear controller

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Particle swarm optimization algorithm for design of an adaptive Kanban system based on optimization via simulation

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年

作者： Elloumi, Khouloud Ammar, Achraf Benaissa, Mounir Univ Tunis Elmanar Natl Engineers Sch Tunis LR OASIS Tunis 1002 Tunisia Int Inst Technol Ind Engn Dept Sfax Tunisia

Manufacturing systems face challenges in adapting to fluctuating demand, making traditional fixed-parameter Kanban systems inefficient. This study addresses these limitations by proposing an adaptive Kanban-based pull control mechanism that dynamically adjusts the number of Kanbans based on inventory levels and backorders. To design an adaptive Kanban system, a metaheuristic optimization approach using particle swarm optimization is integrated with simulation to account for stochastic and time-varying customer demand. Previous studies have either overlooked demand fluctuations in Kanban systems or underutilized the advantages of particle swarm optimization. The results demonstrate that the proposed algorithm significantly reduces total production costs compared to existing metaheuristic methods. These findings underscore the importance of integrating simulation and optimization to enhance decision-making in manufacturing, providing a robust framework for improving production flow control in uncertain environments.

关键词： Manufacturing system adaptive Kanban system pso algorithm metaheuristic optimization discrete event simulation

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Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using pso with Levy flight

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JOURNAL OF ENERGY STORAGE 2022年 49卷

作者： Mao, Xinjian Song, Shaojing Ding, Feng Shanghai Polytech Univ Dept Commun & Informat Engn Shanghai 201201 Peoples R China UAES Dept Syst Shanghai 201201 Peoples R China UAES Elect Control Unit Shanghai 201201 Peoples R China

The battery state of charge (SoC) of lithium batteries for electric vehicles is highly non-linear and time-varying. The convergence speed is slow and the accuracy is low when using ordinary neural network models for SoC estimation. The particle swarm optimization (pso) algorithm based on Levy's flight strategy (Lpso) is proposed to optimize the weights and thresholds of BP neural network, which would improve the prediction accuracy of SoC. According to the mechanism of lithium battery charging and discharging, voltage, current and temperature are selected as input vectors and SoC is selected as output vector. The comparison of the model before and after optimization is carried out by using NASA lithium battery charging and discharging data, which shows this method has better generalization ability and high prediction accuracy. It has practical application significance for SoC estimation.

关键词： SoC estimation Levy flight BP neural network pso algorithm

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SURFACE-TO-AIR MISSILE PATH PLANNING USING GENETIC AND pso algorithmS

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2017年第3期55卷 801-812页

作者： Zandavi, Seid Miad Payam Noor Univ Dept Engn Tehran Iran

Optimization algorithms use various mathematical and logical methods to find optimal points. Given the complexity of models and design levels, this paper proposes a heuristic optimization model for surface-to-air missile path planning in order to achieve the maximum range and optimal height based on 3DOF simulation. The proposed optimization model involves design variables based on the pitch programming and initial pitch angle (boost angle). In this optimization model, we used genetic and particle swarm optimization (pso) algorithms. Simulation results indicated that the genetic algorithm was closer to reality but took longer computation time. pso algorithm offered acceptable results and shorter computation time, so it was found to be more efficient in the surface-to-air missile path planning.

关键词： path planning genetic algorithm pso algorithm surface-to-air missile 3DOF simulation

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Adaptive active queue management controller for TCP communication networks using pso-RBF models

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NEURAL COMPUTING & APPLICATIONS 2013年第5期22卷 933-945页

作者： Sheikhan, Mansour Shahnazi, Reza Hemmati, Ehsan Islamic Azad Univ Dept Commun Engn Fac Engn South Tehran Branch Tehran Iran Islamic Azad Univ Modeling & Optimizat Res Ctr Sci & Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) is a promising technique to congestion control for reducing packet loss and improving network utilization in transmission control protocol (TCP)/Internet protocol (IP) networks. AQM policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts, and the adoption of a suitable control policy. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF as a nonlinear controller is suitable as an AQM scheme to control congestion in TCP communication networks since it has nonlinear behavior. Particle swarm optimization (pso) algorithm is also employed to derive RBF output weights such that the integrated-absolute error is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The output weights and the coefficient of the integral error term in the latter controller are also optimized by pso algorithm. It should be noted that in both proposed controllers the parameters of radial basis functions are selected to symmetrically partition the input space. The results of the comparison with adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

关键词： Active queue management RBF neural network pso algorithm Communication network

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Comparison of RSM, ANN and pso algorithms to Predict the Porosity Level of Twin Wire Arc Sprayed Aluminium Coatings on AZ31B Magnesium Alloy

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International Journal of Vehicle Structures and Systems 2024年第3期16卷 390-395页

作者： Kumar, L. Govind Sanjeev Thirumalaikumarasamy, D. Karthikeyan, K. Mathanbabu, M. Elango, E. Penmetsa, Ravi Varma Dept. of Mfg. Engg Annamalai Uni Tamil Nadu Chidambaram India Dept. of Mech. Engg. Gujarat Inst. Desert Ecology Gujarat Bhuj India Dept. of Mech. Engg. Govt. College of Engg Tamil Nadu Bargur India Dept. of Mech. Engg Annamalai Uni Tamil Nadu Chidambaram India Dept. of Mech. Engg Sagi Rama Krishnam Raju Engg. College Andhra Pradesh Bhimavaram India

The twin wire arc spray method (TWAS) is one among the efficient methods that is widely recognised and utilised for protecting components in surface coating technology due to its economic viability and adaptability. The quality of coating obtained in TWAS is excellent and the porosity is very low in comparison to other conventional methods. The TWAS spray parameters were optimized by single response optimization to obtain the minimum porosity utilizing statistical tools such as DOE and response surface methodology (RSM). Investigations focused on the influence of power, gas pressure and standoff distance. The analysis was done by three factors and five stages of analysis in the central composite design (CCD). The lowest porosity (1.21%) was obtained at optimum conditions of 5 kW power, 3.5 bar air pressure and 200mm stand-off distance, respectively, for TWAS coatings. The RSM, ANN and pso is utilized to optimise the constructed model. The optimized value obtained through pso and ANN were compared with the RSM. ANN gives a superior outcome when comparing pso and RSM. It is validated by the response and contour plots in order to assist in selecting the spray parameters. The obtained results indicate that the coating porosity depends mostly on the arc current. © 2024. Carbon Magics Ltd.

关键词： Aluminium feedstock AZ31B Magnesium alloy pso algorithm Response surface method Twin wire arc spray

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