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multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2019年第4期11卷

作者： Mumtaz, Jabir Li, Zhang Imran, Muhammad Yue, Lei Jahanzaib, Mirza Sarfraz, Shoaib Shehab, Essam Ismail, Sikiru Oluwarotimi Afzal, Kaynat Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Natl Univ Sci & Technol NUST Business Sch Dept Management & HR Islamabad Pakistan Univ Engn & Technol Dept Ind Engn Taxila Pakistan Cranfield Univ Mfg Dept Sch Aerosp Transport & Mfg Cranfield Beds England Univ Hertfordshire Sch Engn & Comp Sci Hatfield Herts England

Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication-assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication-assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.

关键词： Response surface methodology Box-Behnken design multi-objective genetic algorithm end-milling machiningparameters surface roughness power consumption

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Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm

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APPLIED OCEAN RESEARCH 2020年 100卷 102148-102148页

作者： You, Xinxing Hu, Fuxiang Dong, Shuchuang Takahashi, Yuki Shiode, Daisuke Tokyo Univ Marine Sci & Technol Minato Ku 4-5-7 Konan Tokyo 1088477 Japan Univ Tokyo Inst Ind Sci Tokyo 1538505 Japan Hokkaido Univ Fac Fisheries Sci 3-1-1 Minato Cho Hakodate Hokkaido 0418611 Japan

The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bezier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20 degrees. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-todrag ratio before the attack angle of 30 degrees as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25 degrees as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling.

关键词： Cambered otter board Hydrodynamic characteristics Lift-to-drag ratio Neural network multi-objective genetic algorithm PIV

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Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a multi-objective genetic algorithm

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ENERGIES 2020年第12期13卷 3118页

作者： Bi, Qiushi Wang, Guoqiang Wang, Yongpeng Yao, Zongwei Hall, Robert Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Taiyuan Heavy Ind Co LTD Taiyuan 030024 Peoples R China State Key Lab Min Equipment & Intelligent Mfg Taiyuan 030024 Peoples R China Minist Educ Key Lab CNC Equipment Reliabil Changchun 130025 Peoples R China Univ Alberta Sch Min & Petr Engn Edmonton AB T6G 2H5 Canada

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of20 sto10 safter the first phase optimization, and the energy consumption per payload reduced by13.28%after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

关键词： digging trajectory cable shovel robotic excavation multi-objective genetic algorithm

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A multi-objective genetic algorithm based approach for dynamical bus vehicles scheduling under traffic congestion

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 54卷 100667-000页

作者： Wang, Chunlu Shi, Hongyi Zuo, Xingquan Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Many big cities face the problem of traffic congestion, which leads to the planed vehicle scheduling scheme becoming infeasible. It is significant to study bus vehicle scheduling approaches under uncertain environments, such as traffic congestion. In this paper, a bus vehicle scheduling approach is proposed to handle the traffic congestion. It consists of three phases: firstly, a set of candidate vehicle blocks is generated once traffic congestion happens. Secondly, a non-dominated sorting genetic algorithm is adopted to select a subset of vehicle blocks from the set of candidate blocks to generate a set of Non-dominated solutions. Finally, a departure time adjustment procedure is applied to the Non-dominated solutions to further improve the quality of solutions. Experiments on a real-world bus line show that the proposed approach is able to dynamically generate scheduling schemes and significantly improve the quality of service compared to the comparative approaches.

关键词： Bus vehicle scheduling Dynamic vehicle scheduling multi-objective genetic algorithm Urban bus scheduling

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Optimization of Aqueous Two Phase Extraction of Proteins from Litopenaeus Vannamei Waste by Response Surface Methodology Coupled multi-objective genetic algorithm

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CHEMICAL PRODUCT AND PROCESS MODELING 2020年第1期15卷

作者： Pandian, P. Saravana Selvan, S. Sindhanai Subathira, A. Saravanan, S. Natl Inst Technol Dept Chem Engn Tiruchirappalli 620015 Tamil Nadu India

Waste generated from industrial processing of seafood is an enormous source of commercially valuable proteins. One among the underutilized seafood waste is shrimp waste, which primarily consists of head and carapace. Litopenaeus vannamei (L. vannamei) is the widely cultivated shrimp in Asia and contributes to 90 % of aggregate shrimp production in the world. This work was focused on extraction as well as purification of value-added proteins from L. vannamei waste in a single step aqueous two phase system (ATPS). Polyethylene glycol (PEG) and trisodium citrate system were chosen for the ATPS owing to their adequate partitioning and less toxic nature. Response surface methodology (RSM) was implemented for the optimization of independent process variables such as PEG molecular weight (2000 to 6000), pH (6 to 8) and temperature (25 to 45 degrees C). The results obtained from RSM were further validated using a multi-objective genetic algorithm (MGA). At the optimized condition of PEG molecular weight 2000, pH 8 and temperature 35 degrees C, maximum partition coefficient and protein yield were found to be 2.79 and 92.37 %, respectively. Thus, L. vannamei waste was proved to be rich in proteins, which could be processed industrially through cost-effective non-polluting ATPS extraction, and RSM coupled MGA could be a potential tool for such process optimization.

关键词： Litopenaeus vannamei waste aqueous two phase system polyethylene glycol trisodium citrate response surface methodology multi-objective genetic algorithm

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A Three-Stage Surrogate Model Assisted multi-objective genetic algorithm for Computationally Expensive Problems

A Three-Stage Surrogate Model Assisted Multi-Objective Genet...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Jiang, Puyu Zhou, Qi Liu, Jun Cheng, Yuansheng Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728121536

multi-objective optimization problems (MOPs) are commonly encountered in practical engineering. multi-objective evolutionary algorithms (MOEAs) are one of the powerful methods to solve MOPs. However, MOEAs require a large number of fitness evaluations, which limits the practical application of MOEAs. Surrogate model assisted evolutionary algorithm (SAEA) can effectively alleviate the computation burden of MOEAs by replacing time-consuming simulation with the surrogate model. In this paper, a three-stage adaptive multi-fidelity surrogate (MFS) model assisted multi-objective genetic algorithm(MOGA) are proposed. In the first stage, a cheap low-fidelity (LF) model is adopted to obtain a preliminary Pareto frontier (PF). In the second stage, some of the individuals are selected and sent to high-fidelity (HF) model to construct MFS models, which are used to evaluate the fitness functions and sequentially updated according to the model management strategy. During this stage, in order to obtain a better PF, a fidelity control strategy is developed to subjectively determine when transforming is conducted to the third stage, in which all the individuals are evaluated by the HF model. Three benchmark tests are used to test the performance of the proposed method. Results show that the proposed method performs better than online MFS model assisted MOGA( OLMFM-MOGA) and NSGA-II with HF model, especially when the correlation between the LF and HF models is very poor.

关键词： multi-fidelity surrogate model Fidelity control strategy Surrogate model assisted evolutionary algorithm Model management multi-objective genetic algorithm

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Design of a Data-Driven Control System using a multi-objective genetic algorithm 13th

Design of a Data-Driven Control System using a Multi-Objecti...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on Adaptive and Learning Control Systems (ALCOS)

作者： Kinoshita, Takuya Yamamoto, Toru Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima Hiroshima Japan

In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear;hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm data-driven control VRFT

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Optimization Design and Evaluation of Shift Point for Electric Bus Based on multi-objective genetic algorithm 3

Optimization Design and Evaluation of Shift Point for Electr...

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3rd Conference on Vehicle Control and Intelligence (CVCI)

作者： Lei, Yulong Zhang, Jingxu Li, Xingzhong Zhang, Yuchen Fu, Yao Jilin univ State Key Lab Automot Simulat & Control Changchun Peoples R China Jilin univ Qingdao Automobile Res Inst Changchun Peoples R China

ISBN: (纸本)9781728126845

In this paper, an integrated simulation platform of electric bus based on AVL-Cruise and MATLAB is established to provide simulation basis for optimal design of shift point. By extracting the objective function, design variables and constraint conditions, a mathematical model for solving the shift point problem with multiple objectives is established. The NSGA-II algorithm was used to carry out multi-objective optimization design for the up-stop and down-stop points to obtain the pareto optimal solution. The optimal pareto solution was evaluated and analyzed by fuzzy comprehensive evaluation method, and the optimal results of shift MAP based on three cycle conditions were obtained. This paper evaluates and analyzes the optimal results of shift MAP based on working conditions from four aspects of power performance, economy, driving performance and braking energy recovery.

关键词： electric bus mechanical automatic transmission multi-objective genetic algorithm

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Application of response surface method and multi-objective genetic algorithm to configuration optimization of Shell-and-tube heat exchanger with fold helical baffles

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APPLIED THERMAL ENGINEERING 2018年 129卷 512-520页

作者： Wang, Simin Xiao, Juan Wang, Jiarui Jian, Guanping Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

A kind of shell-and-tube heat exchangers with fold baffles was proposed to eliminate the triangular leakage zones betifeen adjacent baffles. An effective algorithm combing second-order polynomial response surface method and multi-objective genetic algorithm was adopted to study the effect of fold baffle configuration parameters on the performance of flow and heat transfer. The helical angle, overlapped degree and shell-side inlet velocity were chosen as design parameters, and the Nusselt number and shell-side pressure drop were considered as objective functions. The results show that both the Nusselt number and shell-side pressure drop increase with the decrease of helical angle and shell-side inlet velocity, and increase with the increasing overlapped degree. A set of Pareto-optimal points were obtained, and the optimization results illustrate a good agreement with CFD simulation data with the relative deviation less than 3%. And the empirical correlations of Nusselt number and friction coefficient were obtained based on response surface method, the helical angle and overlapped degree were fitted into empirical correlations as correction factors for the first time. It is found that the adjusted coefficient of determination of the Nusselt number and friction coefficient is 0.943 and 0.999, respectively, which illustrate the fitting is correct and reliable. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Shell-and-tube heat exchangers Fold helical baffles Triangular leakage zones Response surface method multi-objective genetic algorithm Empirical correlation

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Network optimisation design of Hazmat based on multi-objective genetic algorithm under the uncertain environment

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第4期12卷 236-244页

作者： Ma, Changxi Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

To avoid the hazardous material (Hazmat) transportation accidents, it is necessary to design the Hazmat transportation network in advance. Due to the uncertainty of risks and time during the Hazmat transportation, the paper studies the optimal network design method under the uncertain environment. The transportation scenario is divided into two types including single-vehicle centralised service and multi-vehicle coordinated service. The opportunity constrained programming model for the optimal design of Hazmat transportation network is constructed and the improved multi-objective genetic algorithm is used to solve the model. The case study shows the opportunity constrained programming model can better describe the optimal design of Hazmat transportation network than the traditional method under the uncertain environment. The repeating computer simulation tests show the proposed improved multi-objective genetic algorithm is feasible.

关键词： optimisation transportation network multi-objective genetic algorithm Pareto solution hazmat transportation

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