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High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2018年第6期232卷 734-750页

作者： Mirmohammadsadeghi, Sayed E. Amirabadi, H. Univ Birjand Dept Mech Engn Birjand 97175615 Iran

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

关键词： AISI 304 austenitic stainless steel artificial neural network cutting force response surface method genetic algorithm high-pressure jet-assisted turning surface roughness nondominated sorting genetic algorithm multi-objective optimization

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Multiobjective Optimization of a Dual Stator Brushless Hybrid Excitation Motor Based on Response Surface Model and NSGA 2

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第5期58卷 6105-6114页

作者： Wang, Xu Fan, Ying Lu, Xingchi Chen, Qiushuo Lee, Christopher H. T. Southeast Univ Nanjing 210096 Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

This article proposes a multiobjective optimization design framework for a double stator brushless hybrid excitation motor (DSBHEM) to provide high torque, wide flux regulation ability and low torque ripple. The design variables are divided into sensitive level and insensitive level by sensitivity analysis. The response surface model (RSM) and nondominated sorting genetic algorithm 2 (NSGA2) are organically combined to generate a set of Pareto solutions for the sensitive level variables, from which the optimal values of the sensitive level variables are obtained. In addition, the optimal values of insensitive variables are obtained through single parameter scanning optimization, and a set of final optimal design variables are obtained. The electromagnetic performance of the initial design and the optimal design are compared with finite element analysis. Finally, a prototype is manufactured to verify the proposed concepts.

关键词： Optimization Windings Brushless motors Stator windings Reluctance motors Regulation Iron Brushless hybrid excitation multiobjective optimization nondominated sorting genetic algorithm response surface model sensitivity analysis

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genetic-algorithm-based optimization of an industrial age-hardening operation for packed bundles of aluminum rods

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MATERIALS AND MANUFACTURING PROCESSES 2007年第5-6期22卷 615-622页

作者： Sahay, Satyam S. Mehta, Rajesh Krishnan, Karthik Div Tata Consultancy Serv Ltd Tata Res Dev & Design Ctr Pune 411013 Maharashtra India

Industrial thermal processing operations are highly energy intensive, and must be efficiently operated, especially under the current global energy crisis. However. optimization of such complex operations is a nontrivial task and must be formally approached. In the present work, an integrated model has been used in conjunction with a genetic algorithm to optimize industrial age-hardening of packed bundles of aluminum alloy rods. The presence of peak yield strength and the strong dependence of peak time on age-hardening temperature make this operation interesting for optimization studies. Multiobjective optimization problems have been formulated and solved for energy minimization, productivity maximization, and variability reduction during age-hardening by an elitist nondominated sorting genetic algorithm. The Pareto-front for optimum furnace temperature. time, and quality variation has been determined for isothermal as well as multisegment nonisothermal furnace temperature profiles. Subsequently, optimum multisegment furnace temperature-time set-points were obtained and compared with equivalent isothermal profile. The advantage of multisegment nonisothermal profile over isothermal profile in reducing the energy consumption has been demonstrated.

关键词： age-hardening aluminum alloy energy consumption genetic algorithm integrated model modeling multiobjective nondominated sorting genetic algorithm optimization Pareto-optimality precipitation model productivity product quality thermal model

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Optimal coordination of directional overcurrent relays using NSGA-II

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ELECTRIC POWER SYSTEMS RESEARCH 2015年第Feb.期119卷 228-236页

作者： Moravej, Zahra Adelnia, Farhad Abbasi, Fazel Semnan Univ Fac Elect & Comp Engn Semnan Iran Shahrud Univ Fac Elect Engn Shahrud Iran

This paper presents a new method for coordination of directional overcurrent relays to reduce the discrimination time of the primary and backup relays, by using nondominated sorting genetic algorithm-II (NSGA-II). In order to achieve a reliable protective system, it is necessary to find a solution to avoid having large discrimination times, in addition to coordination time interval (CTI). The novelty of the paper is to reduce the discrimination time of the primary and backup relays and simultaneously reduce the operating time of primary and backup relays, by introducing a new method. In contrast to the conventional intelligent methods, the proposed method does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. The effects of both near and far-end faults are considered to solve the proposed problem formulation. Also, various overcurrent relays characteristics are considered within the program to select the best of them for each relay by optimization algorithm. The proposed method is implemented in three different test cases, i.e. three, eight and IEEE 30-bus network. The results are compared with previously proposed methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Overcurrent relay Optimal coordination Relay characteristics Discrimination time nondominated sorting genetic algorithm Multi-objective function

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Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gain value

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第4期54卷 1079-1084页

作者： Cengiz, Yavuz Konar, Eray Suleyman Demirel Univ Dept Elect & Commun Engn TR-32200 Isparta Turkey

In this work, synthesis of microwave amplifier is put forward as a constrained vector optimization problem whose components are to meet the specific gain value constrained by the minimum noise over the available bandwidth. nondominated solutions are acquired using the nondominated sorting genetic algorithm-II as a fast nondominated genetic sorting algorithm wherefore these synthesis objectives generally conflict with each other. Then, the Pareto frontiers are obtained using these trade-off solution sets between the performance characterization of the device results in the triplet of gain GT, noise F, and input VSWR Vi and its source (ZS) and load (ZL). It can be concluded that the Pareto-optimal synthesis procedure provides microwave amplifier with optimum performance. (c) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:10791084, 2012;View this article online at ***. DOI 10.1002/mop.26724

关键词： microwave amplifier constrained multiobjective optimization Pareto-optimal solutions nondominated sorting genetic algorithm

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Evolutionary Design Automation of High Efficiency Series Resonant Converter for Photovoltaic Systems

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第11期35卷 11332-11343页

作者： Mohammed, Mohammed Sami Vural, Revna Acar Yildiz Tekn Univ Elect & Commun Engn Davutpasa Kampusu TR-34230 Istanbul Turkey

Series resonant converter (SRC) is one of the main parts which has to be designed carefully to be compatible with the general requirements of photovoltaic (PV) grid-connected system. The extensive search space for design parameters of SRC such as resonant tank component values, transformer turn ratio, dead time and switching frequency requires an automated design framework. The proposed framework is developed using genetic algorithm (GA), differential evolution algorithm (DEA), and nondominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization of SRC circuit while accomplishing zero voltage switching (ZVS). Regulating the output voltage while minimizing the losses of MOSFET, diode and transformer are the design objectives. FEM is applied to subdivide the single phase transformer into simpler and finite parts to calculate flux density value according to the optimized design parameters. Amongst these evolutionary algorithms, NSGA-II achieved the best optimization performance considering various operation modes and required specifications. Simulation results are also provided to validate the efficient and robust automated design of SRC to be used in photovoltaic systems.

关键词： Phase transformers Optimization Voltage control Switching frequency genetic algorithms MOSFET Photovoltaic systems nondominated sorting genetic algorithm photovoltaic series resonant converter zero voltage switching

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Optimal design of multiproduct batch chemical plant using NSGA-II

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2006年第1-2期1卷 13-20页

作者： Sarkar, Debasis Modak, Jayant M. Indian Inst Sci Dept Chem Engn Bangalore 560012 Karnataka India

The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. (C) 2006 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： mixed integer nonlinear programming nondominated sorting genetic algorithm multiobjective optimization batch plant design

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Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2018年第6期140卷 1-9页

作者： Liu, Qiong Tian, Youquan Wang, Chao Chekem, Freddy O. Sutherland, John W. Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing 100041 Peoples R China Purdue Univ Environm & Ecol Engn W Lafayette IN 47907 USA

In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint-oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. nondominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.

关键词： flexible job-shop scheduling carbon footprint carbon emission nondominated sorting genetic algorithm

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Optimal DG allocation and sizing in presence of storage systems considering network configuration effects in distribution systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第3期10卷 617-624页

作者： Abbasi, Fazel Hosseini, Seyed Mehdi Shahrood Univ Dept Elect & Comp Engn Shahrood Iran Babol Noshirvani Univ Technol Dept Elect & Comp Engn Babol Sar Iran

Optimal performance of power distribution networks greatly depends on network configuration, location and size of distributed generations (DGs) units and storage systems. That is, for different configurations, different optimal locations and sizes of DGs can be found and vice versa. Therefore, the impact of both location and size of DG and network configuration should be considered in the planning process, simultaneously. Also, the presence of storage systems in the distribution system leads to some loads to be supplied even in fault conditions. In this study, distribution system reconfiguration (DSR), for considering network configuration effect that runs in offline mode with constant loads, and optimal DG allocation and sizing problems are studied simultaneously to find an optimal condition for distribution network based on operational thresholds and reliability improvements. Non-dominated sorting genetic algorithm is used to solve these problems simultaneously. Power losses, energy not supplied (ENS) and the costs associated with DG are the objectives that are studied. The method of calculating ENS in DSR problem in the presence of DGs with storage systems is explained and the impact of protective equipment is considered, as well. The proposed approach is applied on different test systems, and its effectiveness is shown in various conditions.

关键词： distributed power generation power distribution planning power distribution reliability genetic algorithms power distribution faults optimal DG allocation problem optimal DG sizing problem network configuration effects optimal power distribution network performance distributed generation units distributed generation storage systems planning process fault conditions distribution system reconfiguration offline mode constant loads operational thresholds reliability improvements nondominated sorting genetic algorithm power losses energy not supplied ENS calculation protective equipment test systems

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Peak Colocalized Orthogonal Matching Pursuit for Seismic Trace Decomposition

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IEEE ACCESS 2020年 8卷 8620-8626页

作者： Li, Yongqing Wang, Jun Li, Hui Ren, Peng China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

Orthogonal matching pursuit (OMP) is an efficient method for decomposing a seismic trace with regard to an atom dictionary. The original OMP optimizes one unique single objective in terms of successively maximizing the inner product between an atom and its corresponding residual at different approximating levels. Though the inner product effectively measures signal similarity at a global time scale, it tends to neglect localizing an atom whose peak position plays a key role in seismic reconstruction. To address this limitation, we propose a peak colocalized orthogonal matching pursuit (PCOMP) strategy that optimizes two objectives, i.e., signal correlation and peak colocalization, both of which are defined based on signal residuals and atoms. Compared with the original OMP, the PCOMP extends a much larger search space in favor of more accurate seismic reconstruction. In this scenario, the genetic algorithm (GA) used for solving the original OMP is not suitable for the PCOMP. Therefore, we propose to solve the two objective optimization problem by exploiting an improved nondominated sorting genetic algorithm (NSGA-II) algorithm, which not only increases the diversity of searching for optimization and but also reduces the reconstruction error over the GA. Furthermore, the constrained atom positions obtained from the peak colocalization objective enable efficient convergence. Experiments for seismic data validate the advantages of the proposed PCOMP.

关键词： Orthogonal matching pursuit peak colocalization nondominated sorting genetic algorithm multiobjective optimization

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