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Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第20期232卷 3573-3594页

作者： Shrivastava, Yogesh Singh, Bhagat Jaypee Univ Engn & Technol Dept Mech Engn AB Rd Guna 473226 Madhya Pradesh India

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.

关键词： Tool chatter empirical mode decomposition intrinsic mode function response surface methodology multi-objective genetic algorithm

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Optimization of the Structural Parameters of a Cyclone by Using a genetic algorithm

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CHEMICAL ENGINEERING & TECHNOLOGY 2022年第9期45卷 1691-1700页

作者： Song, Chen Wang, Jiarui Wang, Simin Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol 28 Xianning VVest Rd Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

A mathematical model is proposed to optimize a two-stage separation cyclone. The model combines the discrete phase model, multi-objective genetic algorithm, and response surface model. The model was validated by experimental data with an average deviation of 9.87 %. The effects of three structural parameters on the comprehensive performance of this cyclone are discussed. By applying a central composite design method, the cyclone was optimized and three optimal designs were obtained, with an average pressure drop reduction of 6.05 %, and 2.34-fold improvement in separation efficiency over the original design. The proposed model and results are of great significance for the further design of this type of cyclone.

关键词： multi-objective genetic algorithm Optimal design Structural parameters Two-stage separation Ultrafine particles

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Thermo-economic triple-objective optimization of a solar chimney power plant using genetic algorithms

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ENERGY 2014年 70卷 204-211页

作者： Gholamalizadeh, Ehsan Kim, Man-Hoe Kyungpook Natl Univ Sch Mech Engn Taegu South Korea

A triple-objective design method was developed for a solar chimney power plant system that simultaneously optimizes the expenditure, total efficiency, and power output. A multi-objective genetic algorithm was used to obtain the best combination of geometric parameters of the power plant. The following design parameters were selected: collector radius, chimney height, and chimney diameter. Two different solar chimney power plant configurations were considered: the Kerman pilot power plant and Manzanares prototype power plant. A set of possible optimal solutions (Pareto optimal set) was obtained. Based on the optimal solutions, the best configuration for each power plant was selected. The performance and expenditure of the optimal solutions and the built power plants were compared. The results showed that the increment of the power output was higher than the increment of the expenditure in the optimal configuration. A parametric study was conducted to evaluate the effects of changing design parameters on different objective functions. This paper provides a very useful design and optimization methodology for solar chimney power plant systems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar chimney power plant Renewable energy Thermo-economic analysis Optimization multi-objective genetic algorithm

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An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2010年第17期48卷 5157-5179页

作者： Akgunduz, Onur Serkan Tunali, Semra Dokuz Eylul Univ Dept Ind Engn Izmir Turkey

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.

关键词： mixed-model assembly line sequencing mixed-model sequencing genetic algorithm multi-objective genetic algorithm

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An improved cellular automata model of enzyme kinetics based on genetic algorithm

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CHEMICAL ENGINEERING SCIENCE 2014年 110卷 105-118页

作者： Kar, Saurajyoti Nag, Kaustuv Dutta, Abhishek Constales, Denis Pal, Tandra Natl Inst Technol Dept Biotechnol Durgapur 713209 W Bengal India Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 W Bengal India Associatie KU Leuven Dept Chem Engn Grp Leuven Engn Coll T B-3000 Louvain Belgium Univ Ghent Dept Math Anal B-9000 Ghent Belgium

Based on a generalized Cellular Automata (CA) model which has the flexibility of reaction interactions between all possible two-agent combinations on both reactant and product side, in previous studies by the authors, probabilistic reaction events were combined in a multiplicative order and an exploratory study of the variability analysis was performed. However it was found that the probabilistic parameter values are extremely sensitive to the final model output. As such, NSGA-II based on a multi-objective genetic algorithm (MOGA) is used in this study to obtain optimum sets of these probability parameter values within a feasible computational time. For each generation of the MOGA, the probability rules parameter values are updated (improved) based on the objective functions. Finally, a validation study is performed to indicate the applicability of the CA model to represent specific enzymatic reactions when coupled with the multi-objective optimization algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Cellular automata multi-objective genetic algorithm NSGA-II Enzyme kinetics

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Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis

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ENERGY 2019年 171卷 37-48页

作者： Sun, Henan Ge, Ya Liu, Wei Liu, Zhichun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are considered on the top of the upper substrate. In the optimization process, the specific power and entropy generation rate are considered synchronously as objective functions to maximize the power output per unit area and to minimize the irreversibilities. The FEM is adopted in the simulation model, and the Seebeck effect, together with the Peltier effect, Joule heating, Thomson effect, and Fourier heat conduction phenomena are all considered in the simulation process. Shannon's entropy method is applied to select the best solution from the Pareto Frontier. Besides, the exergy destruction rate is analyzed, the results show that the exergy destruction rate increases as the load resistance increases. In addition, the different relationships between the load resistance and the voltage, power output, efficiency and entropy generation rate are presented. The principle of performance enhancement is also explained by comparing the ZT value along the TE legs. The optimization is important to the development of more compact and high-efficiency thermoelectric generators. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Thermoelectric generator multi-objective genetic algorithm Specific power Entropy generation rate Exergy destruction rate

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An innovative design for floating breakwater with multi-objective genetic optimal method

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OCEAN ENGINEERING 2024年 312卷

作者： Mao, Puxiu Chen, Cheng Chen, Xinquan Zhang, Qi Bao, Yan Yang, Qi Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Underwater Engn Inst Co Ltd Shanghai 200231 Peoples R China

The hydro-meteorological conditions in large port terminals are complex, severely hindering the safe berthing and operations of ships;in this case, floating breakwaters are suitable alternatives to traditional fixed facilities because of their advantages such as maneuverability, adaptability, minimal impact on marine environments, and convenient construction and transportation. In this study, a floating breakwater with wing-plate was indicated, and the influence of the structural dimensions on wave attenuation performance is explored based on potential flow theory. Additionally, a parameterized scaling optimization method based on a multi-objective genetic algorithm (MOGA) was introduced and applied to the newly designed floating breakwater. Then a numerical hydrodynamic model was constructed according to the optimization result, and computational fluid dynamics (CFD) simulations were conducted using STAR-CCM+. Finally, wave dissipation experiments were performed and validated the superiority wave attenuation performance of the innovative floating breakwater over the traditional square-box floating breakwater.

关键词： Floating breakwater Wave attenuation performance Parametric optimization multi-objective genetic algorithm Computational fluid dynamics

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Estimation of absolute distance and high-frequency vibration from the modulated SM-OFI signal using compound mutated genetic algorithm

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OPTICS AND LASERS IN ENGINEERING 2020年 134卷 106271-106271页

作者： Bhardwaj, Vibhor Kumar Maini, Surita St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India Bhagwant Inst Technol Dept Elect & Commun Engn Muzaffarnagar 251315 Uttar Pradesh India

Contactless measurement of absolute distance or high-frequency vibration is a prominent factor for many industrial & scientific applications like predictive maintenance, non-destructive testing, and reverse engineering, etc. In recent years, self-mixing optical feedback interferometry (SM-OFI) has proven its superiority over other conventional optical methods because of its simple, compact and less expensive structure. Typically, the extraction of the sensing parameters from the modulated SM-OFI signal relies on the analysis of interferometric fringes that often leads to a non-optimal solution for weak feedback conditions. In this paper, the authors introduce a novel method to process the SM-OFI signal that employs a multi-objective compound mutated genetic algorithm to search for optimum solutions. To the author's knowledge, this is the first attempt to measure the vibrating frequency and the absolute distance between the laser-diode front facet and the vibrating target, simultaneously using a multi-objective global optimization method instead of frequency analysis. The validity of the proposed method was experimentally tested on a vibrating object located 2 to 20 cm away from the laser diode and having a vibration frequency between 2 and 10 kHz. The proposed method exhibits an excellent accuracy of 1% & 0.98% with an SNR of 42 dB & 19 dB for absolute distance & vibration-frequency measurement, respectively. The method also possesses a resolution of 0.6 mm during absolute distance measurement and a percentage of error ranging between 2.3 to 8% during high-frequency vibration measurement, both under the weak feedback conditions. The comparison with other methods also supports the potential of the proposed method for the applications where absolute distance and frequency of the vibrations are required to measure simultaneously.

关键词： Self-mixing effect Optical feedback multi-objective genetic algorithm Compound mutation Vibration frequency estimation Distance estimation

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Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

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Chinese Physics C 2017年第2期41卷 166-176页

作者：焦毅徐刚 Key Laboratory of Particle Acceleration Physics and Technology Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China

In the lattice design of a diffraction-limited storage ring（DLSR） consisting of compact multi-bend achromats（MBAs）, it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization（MOPSO） and multi-objective genetic algorithm（MOGA）. For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima.

关键词： diffraction-limited storage ring High Energy Photon Source multi-objective particle swarm optimization multi-objective genetic algorithm lattice design

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A genetic optimization of a hybrid organic Rankine plant for solar and low-grade energy sources

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ENERGY 2015年 91卷 807-815页

作者： Scardigno, Domenico Fanelli, Emanuele Viggiano, Annarita Braccio, Giacobbe Magi, Vinicio Univ Basilicata Sch Engn I-85100 Potenza Italy ENEA Tech Unit Trisaia Technol UTTri I-75026 Rotondella Italy

This paper provides a multi-objective optimization of a hybrid organic Rankine plant for solar and low-grade energy sources. In this plant, water, with a mass flow rate of 1 kg/s at a temperature of 90 degrees C, preheats the working fluid. The objective of this work is to search for solutions with the highest first and second law efficiencies of the plant and the lowest LEC (levelized energy cost). The design parameters are i) the working fluid, ii) the evaporating and condensing pressure, iii) the maximum temperature of the collector thermal fluid and iv) a parameter representative of the temperature profiles in the heat exchangers. A NSGAII (non-dominated sorting genetic algorithm) has been used. The Pareto front solutions provide Cyclopropane, R143a and R32 as working fluids. The lowest LEC (0.114 $/kWh) and the highest first law efficiency (9.65%) are achieved by using Cyclopropane, with a power output greater than 100 kW. The highest second law efficiency (44%) is obtained by employing R143a, with a low contribution of the solar source and a power output greater than 10 kW. Finally, R32 solutions have comparable performance with respect to R143a solutions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Organic Rankine cycle multi-objective genetic algorithm Low-grade energy Solar energy Thermo-economic analysis

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