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Modelling and optimization of thermal sprayed Stellite-6 coating on steel substrate

INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-18页

作者： Hajipour, Mohammad Mirshekari, Erfan Shahrooi, Shahram Islamic Azad Univ Dept Mech Engn Ahvaz Branch Ahvaz Iran

This study presents a simulation of the thermal spraying process of Stellite-6 microparticles onto a steel substrate using the finite element method. The investigation focuses on the effects of particle size, substrate pre-heating temperature, and spraying angle on key response variables such as stress, equivalent plastic strain (PEEQ), penetration depth, and temperature distribution at the particle-substrate interface. Among these, the penetration depth of particles significantly influences the bonding strength of the resulting coatings. Therefore, optimizing the process input parameters in order to maximize the penetration depth is essential for achieving high-quality coatings with strong adhesion to the substrates. To systematically design and conduct the simulations, the Design of Experiments (DOE) approach was employed. Subsequently, response surface methodology (RSM) was applied to examine the relationships between the input parameters and the resulting outputs. The simulation findings demonstrate a strong dependence of penetration depth on particle size, with the spraying angle also emerging as a critical factor. Specifically, an increase in particle size and spraying angle leads to greater penetration depth. Furthermore, process input parameters were optimized using the bees algorithm, with the established relationship for penetration depth serving as the objective function. The primary outcome of this optimization process was the maximization of penetration depth through the application of the bees algorithm. Optimization using the bees algorithm yielded a maximum penetration depth of 5.10E-03 mm corresponding to a particle size of 53 mu m, substrate temperature of 373 K, and spraying angle of 86.3 degrees, validating the efficiency of the proposed approach. The significance of this study lies in its systematic integration of advanced simulation and optimization techniques to enhance bonding strength in thermal spray coatings.

关键词： Thermal spraying Finite element Stellite-6 bees algorithm Response surface methodology

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A Comparative Study of Artificial Bee Colony, bees algorithms and Differential Evolution on Numerical Benchmark Problems

A Comparative Study of Artificial Bee Colony, Bees Algorithm...

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5th International Symposium on Intelligence Computation and Applications

作者： Li, Huanzhe Liu, Kunqi Li, Xia Shijiazhuang Univ Econ Dept Comp Sci Shijiazhuang 050031 Peoples R China

ISBN: (纸本)9783642163876

In this paper, performance of basic Artificial Bee Colony, bees and Differential evolution algorithms is compared on eight well-known benchmark problems. Most of experimental results show that the DE/best/1/exp scheme has the best performance on unimodal problems, bees algorithm has the second performance except Quadric and Rosenbrock functions. On multimodal problems bees algorithm has the best performance, and Artificial Bee Colony is the second.

关键词： Artificial bee colony algorithm bees algorithm Differential evolution Numerical optimization

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Robotic bees' Swarm Autonomy

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IEEE SPECTRUM 2024年第7期61卷 14-15页

作者： Jones, Willie D.

The latest buzz in biomimetic robots is a robotic bee like this one. The BionicBee, produced by the automation-technology company Festo, based in Esslingen am Neckar, Germany, is an order of magnitude bigger than a real bee. For a sense of a BionicBee's size, imagine a bluejay or an oversized hummingbird, albeit one whose wings flap forward and back (closer to the movements of a real bee) rather than up and down. The company says its swarm technology will enable the drone to operate autonomously while it coordinates its flight path with other “bees,” using ultrawideband beacons whose signals let it keep track of its position in space and avoid midair collisions.

关键词： Biomimetics bees algorithm Collision avoidance Autonomous systems Companies Aerospace control Path planning

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Using bees Hill Flux Balance Analysis (BHFBA) for in silico Microbial Strain Optimization

Using Bees Hill Flux Balance Analysis (BHFBA) for <i>in sili...

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5th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Choon, Yee Wen Bin Mohamad, Mohd Saberi Deris, Safaai Illias, Rosli Md Chai, Lian En Chong, Chuii Khim Univ Teknol Malaysia Fac Comp Sci & Informat Syst Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Chem Engn Dept Bioproc Engn Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642365461

Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed bees Hill Flux Balance Analysis (BHFBA) which is a hybrid of bees algorithm, Hill Climbing algorithm and Flux Balance Analysis to solve the problems and improve the performance in predicting optimal sets of gene deletion for maximizing the growth rate and production yield of desired metabolite. Escherichia coli is the model organism in this paper. The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BHFBA performed better in term of computational time, stability and production yield.

关键词： bees algorithm Hill Climbing Flux Balance Analysis Microbial Strains Optimization

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Design Optimization Comparison of BLPM Traction Motor using bees and Genetic algorithms

Design Optimization Comparison of BLPM Traction Motor using ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Braiwish, N. Y. Anayi, F. J. Fahmy, A. A. Eldukhri, E. E. Cardiff Univ Sch Engn Wolfson Ctr Magnet Cardiff CF10 3AX S Glam Wales Cardiff Univ Inst Mech & Mfg Engn Cardiff CF10 3AX S Glam Wales

ISBN: (纸本)9781479978007

Electric machines designs for traction applications are concerned with high power density, high efficiency and low manufacturing cost. In order to achieve better combination of parameters that satisfy the machine electromagnetics, mechanical, and thermal designs limitations;an optimization algorithm is very necessary. Therefore;this work focuses on comparing two optimization algorithms that are utilized to search for optimal solution to design Brushless Permanent Magnet (BLPM) Motor. The target machine design has to meet with FReedomCAR specifications as detailed by UQM Technologies Inc. In this paper, a relatively new modern evolutionary optimization technique known as the bees algorithm (BA) employed to search for the optimum design parameters according to a pre-defined multiobjective function. The results from BA have been compared with the other results obtained from Genetic algorithm (GA). Therefore, both optimization methods have been compared in terms of computational efficiency and ability to find optimal solution for BLPM. However, the comparison results show that BA has better computational ability to search for optimal parameters and achieves lower average value for the objective function than the GA.

关键词： bees algorithm Brushless Permanent Magnet Motor BLPM Genetic algorithm fitness value Multi-objective function

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bees for Block Matching

Bees for Block Matching

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Boumazouza, Daoud Sefouane, Yasmine Djeddi, Mohamed Khelouat, Boualem Benatchba, Karima Ecole Natl Super Informat ESI Lab Methodes Concept Syst Algiers Algeria

ISBN: (纸本)9781479902248

Detection of moving objects in a video sequence is a growing field, used in various domains. There are, in the literature, several approaches to detect such movements. In this paper, we will focus on one of these approaches namely block matching. We propose to speed the performances of the block matching algorithm using the bees' algorithm which is known for its efficient exploration of search space as it uses two interesting strategies intensification and diversification. The proposed algorithm, BAforBM, is evaluated and compared to two existing ones: genetic bloc matching algorithm and a block matching algorithm based on PSO (Particule Swarm Optimisation).

关键词： Motion detection Block matching bees algorithm metaheuristic

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Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1380-1393页

作者： Haitao Yuan Meng Chu Zhou Qing Liu Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21589Saudi Arabia IEEE

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent *** scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy *** factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial *** dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial *** work adopts a G/G/1 queuing system to analyze the performance of servers in *** on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all *** databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

关键词： bees algorithm data centers distributed green cloud(DGC) energy optimization intelligent optimization simulated annealing task scheduling machine learning

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Object-Based Distinction between Building Shadow and Water in High-Resolution Imagery Using Fuzzy-Rule Classification and Artificial Bee Colony Optimization

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JOURNAL OF SENSORS 2016年第7-11期2016卷 1-10页

作者： He, Yuanrong Zhang, Xinxin Hua, Lizhong Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361024 Peoples R China

Due to the high similarity of the spectra of urban water and building shadows, high-resolution satellite imagery often confuses and wrongly classifies these features. To address this problem, we propose an object-based method for distinguishing building shadow from water using an artificial bee colony algorithm. In the method, four spectral ratio bands are first calculated as additional input parameters for improving the accuracy of segmentation results. During the segmentation, a series of statistical factors, such as spectrum, ratio, and sharp features, are calculated to make up for defects in the high-resolution imagery. Finally, we propose a fuzzy-rule-based classifier to generate extraction rules. The classifier is based on artificial bee colony optimization, which employs the geometric mean (G-mean) as fitness function. The proposed method was carried out on two test sites in Xiamen City. The experimental results based on GF-1 satellite date show that, compared with SVM method, the proposed method improved the overall accuracy of extraction by approximately 6% to 15% and the kappa coefficient values by approximately 0.1 to 0.2. The analysis of the extraction rules also proves that the red/NIR band and the length-width ratio band are significantly influenced by the distinction between building shadow and water.

关键词： High resolution imaging bees algorithm Municipal water supply Shadow (Psychoanalysis) Cohen’s kappa coefficient (Statistics)

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Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第8期29卷 1923-1940页

作者： Vejdannik, Masoud Sadr, Ali IUST Sch Elect Engn Tehran 16844 Iran

During the gas tungsten arc welding of nickel based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as. and d phases can precipitate in the microstructure, during aging at high temperatures. However, it is possible to minimize the formation of the Nb- rich Laves phases and therefore reduce the possibility of solidification cracking by adopting the appropriate welding conditions. This paper aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb- base alloy, thermally aged at 650 and 950. C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4MHz. The ultrasound signals are inherently non- linear and thus the conventional linear time and frequency domain methods can not reveal the complexity of these signals clearly. Consequently, an automated processing system is designed using the higher- order statistics techniques, such as 3rd- order cumulant and bispectrum. These techniques are non- linear methods which are highly robust to noise. For this, the coefficients of 3rd- order cumulant and bispectrum of ultrasound signals are subjected to the independent component analysis (ICA) technique to reduce the statistical redundancy and reveal discriminating features. These dimensionality reduced features are fed to the probabilistic neural network (PNN) to automatic microstructural classification. The training process of PNN depends only on the selection of the smoothing parameters of pattern neurons. In this article, we propose the application of the bees algorithm to the automatic adaptation of smoothing parameters. The ICA components of cumulant coefficients coupled with the optimized PNN yielded the highest average accuracy of 97.0 and 83.5%, respectively for thermal aging at 650 and 950. C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound

关键词： bees algorithm Higher-order statistics Independent component analysis Nondestructive inspection Probabilistic neural network Ultrasound signals

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Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

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ANNALS OF TELECOMMUNICATIONS 2010年第9-10期65卷 625-633页

作者： Shermeh, Ataollah Ebrahimzadeh Azimi, Hamed Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

Automatic modulation recognition plays an important role for many novel computer and communication technologies. Most of the proposed systems can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal-to-noise ratio. In this paper, we present a novel hybrid intelligent system that automatically recognizes a variety of digital signals. In this recognizer, a multilayer perceptron neural network with resilient back propagation learning algorithm is proposed as the classifier. For the first time, a combination set of spectral features and higher order moments up to eighth and higher order cumulants up to eighth are proposed as the effective features. Then we have optimized the classifier design by bees algorithm (BA) for selection of the best features that are fed to the classifier. This optimization method is new for this area. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA.

关键词： Communication format classification bees algorithm Resilent back propagation training algorithm Multilayer perceptron Spectral features Higher order moments Higher order cumulants

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